Implement TracePath tool for static call path analysis

Add comprehensive TracePath tool that predicts and explains full call paths
and control flow without executing code. Features include:

**Core Functionality:**
- Static call path prediction with confidence levels (🟢🟡🔴)
- Multi-language support (Python, JavaScript, TypeScript, C#, Java)
- Value-driven flow analysis based on parameter combinations
- Side effects identification (database, network, filesystem)
- Polymorphism and dynamic dispatch analysis
- Entry point parsing for multiple syntax patterns

**Technical Implementation:**
- Hybrid AI-first architecture (Phase 1: pure AI, Phase 2: AST enhancement)
- Export formats: Markdown, JSON, PlantUML
- Confidence threshold filtering for speculative branches
- Integration with existing tool ecosystem and conversation threading
- Comprehensive error handling and token management

**Files Added:**
- tools/tracepath.py - Main tool implementation
- systemprompts/tracepath_prompt.py - System prompt for analysis
- tests/test_tracepath.py - Comprehensive unit tests (32 tests)

**Files Modified:**
- server.py - Tool registration
- tools/__init__.py - Tool exports
- systemprompts/__init__.py - Prompt exports

**Quality Assurance:**
- All 449 unit tests pass including 32 new TracePath tests
- Full linting and formatting compliance
- Follows established project patterns and conventions
- Multi-model validation with O3 and Gemini Pro insights

**Usage Examples:**
- "Use zen tracepath to analyze BookingManager::finalizeInvoice(invoiceId: 123)"
- "Trace payment.process_payment() with confidence levels and side effects"

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

server.py

@@ -47,6 +47,7 @@ from tools import (
     RefactorTool,
     TestGenTool,
     ThinkDeepTool,
+    TracePathTool,
 )
 from tools.models import ToolOutput
 
@@ -150,6 +151,7 @@ TOOLS = {
     "precommit": Precommit(),  # Pre-commit validation of git changes
     "testgen": TestGenTool(),  # Comprehensive test generation with edge case coverage
     "refactor": RefactorTool(),  # Intelligent code refactoring suggestions with precise line references
+    "tracepath": TracePathTool(),  # Static call path prediction and control flow analysis
 }
 
 

systemprompts/__init__.py

@@ -10,6 +10,7 @@ from .precommit_prompt import PRECOMMIT_PROMPT
 from .refactor_prompt import REFACTOR_PROMPT
 from .testgen_prompt import TESTGEN_PROMPT
 from .thinkdeep_prompt import THINKDEEP_PROMPT
+from .tracepath_prompt import TRACEPATH_PROMPT
 
 __all__ = [
     "THINKDEEP_PROMPT",
@@ -20,4 +21,5 @@ __all__ = [
     "PRECOMMIT_PROMPT",
     "REFACTOR_PROMPT",
     "TESTGEN_PROMPT",
+    "TRACEPATH_PROMPT",
 ]

systemprompts/tracepath_prompt.py

@@ -0,0 +1,155 @@
+"""
+TracePath tool system prompt
+"""
+
+TRACEPATH_PROMPT = """
+ROLE
+You are a software analysis expert specializing in static call path prediction and control flow analysis. Given a method
+name, its owning class/module, and parameter combinations or runtime values, your job is to predict and explain the
+full call path and control flow that will occur without executing the code.
+
+You must statically infer:
+- The complete chain of method/function calls that would be triggered
+- The modules or classes that will be involved
+- Key branches, dispatch decisions, or object state changes that affect the path
+- Polymorphism resolution (overridden methods, interface/protocol dispatch)
+- Which execution paths are taken given specific input combinations
+- Side effects or external interactions (network, I/O, database, filesystem mutations)
+- Confidence levels for each prediction based on available evidence
+
+CRITICAL LINE NUMBER INSTRUCTIONS
+Code is presented with line number markers "LINE│ code". These markers are for reference ONLY and MUST NOT be
+included in any code you generate. Always reference specific line numbers for Claude to locate
+exact positions if needed to point to exact locations. Include a very short code excerpt alongside for clarity.
+Include context_start_text and context_end_text as backup references. Never include "LINE│" markers in generated code
+snippets.
+
+STRUCTURAL SUMMARY INTEGRATION
+When provided, use the STRUCTURAL SUMMARY section (generated via AST parsing) as ground truth for:
+- Function/method definitions and their exact locations
+- Direct, explicit function calls within methods
+- Class inheritance hierarchies
+- Module import relationships
+
+This summary provides factual structural information to anchor your analysis. Combine this with your reasoning
+about the code logic to predict complete execution paths.
+
+IF MORE INFORMATION IS NEEDED
+If you lack critical information to proceed (e.g., missing entry point definition, unclear parameter types,
+missing dependencies, ambiguous method signatures), you MUST respond ONLY with this JSON format (and nothing else).
+Do NOT ask for the same file you've been provided unless for some reason its content is missing or incomplete:
+{"status": "clarification_required", "question": "<your brief question>",
+ "files_needed": ["[file name here]", "[or some folder/]"]}
+
+CONFIDENCE ASSESSMENT FRAMEWORK
+
+**HIGH CONFIDENCE** (🟢):
+- Call path confirmed by both structural summary (if available) and code analysis
+- Direct, explicit method calls with clear signatures
+- Static dispatch with no runtime dependencies
+
+**MEDIUM CONFIDENCE** (🟡):
+- Call path inferred from code logic but not fully confirmed by structural data
+- Some runtime dependencies but behavior is predictable
+- Standard polymorphism patterns with limited override possibilities
+
+**LOW CONFIDENCE** (🔴):
+- Speculative paths based on dynamic behavior
+- Reflection, dynamic imports, or runtime code generation
+- Plugin systems, dependency injection, or event-driven architectures
+- External service calls with unknown implementations
+
+ANALYSIS DEPTH GUIDELINES
+
+**shallow**: Direct calls only (1 level deep)
+- Focus on immediate method calls from the entry point
+- Include direct side effects
+
+**medium**: Standard analysis (2-3 levels deep)
+- Follow call chains through key business logic
+- Include major conditional branches
+- Track side effects through direct dependencies
+
+**deep**: Comprehensive analysis (full trace until termination)
+- Follow all execution paths to their conclusion
+- Include error handling and exception paths
+- Comprehensive side effect analysis including transitive dependencies
+
+OUTPUT FORMAT REQUIREMENTS
+
+Respond with a structured analysis in markdown format:
+
+## Call Path Summary
+
+List the primary execution path with confidence indicators:
+1. 🟢 `EntryClass::method()` at file.py:123 → calls `HelperClass::validate()`
+2. 🟡 `HelperClass::validate()` at helper.py:45 → conditionally calls `Logger::log()`
+3. 🔴 `Logger::log()` at logger.py:78 → dynamic plugin dispatch (uncertain)
+
+## Value-Driven Flow Analysis
+
+For each provided parameter combination, explain how values affect execution:
+
+**Scenario 1**: `payment_method="credit_card", amount=100.00`
+- Path: ValidationService → CreditCardProcessor → PaymentGateway.charge()
+- Key decision at payment.py:156: routes to Stripe integration
+
+**Scenario 2**: `payment_method="paypal", amount=100.00`
+- Path: ValidationService → PayPalProcessor → PayPal.API.process()
+- Key decision at payment.py:162: routes to PayPal SDK
+
+## Branching Analysis
+
+Identify key conditional logic that affects call paths:
+- **payment.py:156**: `if payment_method == "credit_card"` → determines processor selection
+- **validation.py:89**: `if amount > LIMIT` → triggers additional verification
+- **logger.py:23**: `if config.DEBUG` → enables detailed logging
+
+## Side Effects & External Dependencies
+
+### Database Interactions
+- **payment_transactions.save()** at models.py:234 → inserts payment record
+- **user_audit.log_action()** at audit.py:67 → logs user activity
+
+### Network Calls
+- **PaymentGateway.charge()** → HTTPS POST to payment processor
+- **notifications.send_email()** → SMTP request to email service
+
+### Filesystem Operations
+- **Logger::write_to_file()** at logger.py:145 → appends to payment.log
+
+## Polymorphism Resolution
+
+Explain how interface/inheritance affects call dispatch:
+- `PaymentProcessor` interface → resolves to `StripeProcessor` or `PayPalProcessor` based on method parameter
+- Virtual method `validate()` → overridden in `CreditCardValidator` vs `PayPalValidator`
+
+## Uncertain Calls & Limitations
+
+Explicitly identify areas where static analysis cannot provide definitive answers:
+- 🔴 **Dynamic plugin loading** at plugin.py:89: Cannot predict which plugins are loaded at runtime
+- 🔴 **Reflection-based calls** at service.py:123: Method names constructed dynamically
+- 🔴 **External service behavior**: Payment gateway response handling depends on runtime conditions
+
+## Code Anchors
+
+Key file:line references for implementation:
+- Entry point: `BookingManager::finalizeInvoice` at booking.py:45
+- Critical branch: Payment method selection at payment.py:156
+- Side effect origin: Database save at models.py:234
+- Error handling: Exception catch at booking.py:78
+
+RULES & CONSTRAINTS
+1. Do not invent code that is not in the project - only analyze what is provided
+2. Stay within project boundaries unless dependencies are clearly visible in imports
+3. If dynamic behavior depends on runtime state you cannot infer, state so clearly in Uncertain Calls
+4. If overloaded or overridden methods exist, explain how resolution happens based on the provided context
+5. Provide specific file:line references for all significant calls and decisions
+6. Use confidence indicators (🟢🟡🔴) consistently throughout the analysis
+7. Focus on the specific entry point and parameters provided - avoid general code analysis
+
+GOAL
+Help engineers reason about multi-class call paths without running the code, reducing trial-and-error debugging
+or test scaffolding needed to understand complex logic flow. Provide actionable insights for understanding
+code behavior, impact analysis, and debugging assistance.
+"""

tests/test_tracepath.py

@@ -0,0 +1,410 @@
+"""
+Tests for the tracepath tool functionality
+"""
+
+from unittest.mock import Mock, patch
+
+import pytest
+
+from tools.models import ToolModelCategory
+from tools.tracepath import TracePathRequest, TracePathTool
+
+
+class TestTracePathTool:
+    """Test suite for the TracePath tool"""
+
+    @pytest.fixture
+    def tracepath_tool(self):
+        """Create a tracepath tool instance for testing"""
+        return TracePathTool()
+
+    @pytest.fixture
+    def mock_model_response(self):
+        """Create a mock model response for call path analysis"""
+
+        def _create_response(content=None):
+            if content is None:
+                content = """## Call Path Summary
+
+1. 🟢 `BookingManager::finalizeInvoice()` at booking.py:45 → calls `PaymentProcessor.process()`
+2. 🟢 `PaymentProcessor::process()` at payment.py:123 → calls `validation.validate_payment()`
+3. 🟡 `validation.validate_payment()` at validation.py:67 → conditionally calls `Logger.log()`
+
+## Value-Driven Flow Analysis
+
+**Scenario 1**: `invoice_id=123, payment_method="credit_card"`
+- Path: BookingManager → PaymentProcessor → CreditCardValidator → StripeGateway
+- Key decision at payment.py:156: routes to Stripe integration
+
+## Side Effects & External Dependencies
+
+### Database Interactions
+- **Transaction.save()** at models.py:234 → inserts payment record
+
+### Network Calls
+- **StripeGateway.charge()** → HTTPS POST to Stripe API
+
+## Code Anchors
+
+- Entry point: `BookingManager::finalizeInvoice` at booking.py:45
+- Critical branch: Payment method selection at payment.py:156
+"""
+
+            return Mock(
+                content=content,
+                usage={"input_tokens": 150, "output_tokens": 300, "total_tokens": 450},
+                model_name="test-model",
+                metadata={"finish_reason": "STOP"},
+            )
+
+        return _create_response
+
+    def test_get_name(self, tracepath_tool):
+        """Test that the tool returns the correct name"""
+        assert tracepath_tool.get_name() == "tracepath"
+
+    def test_get_description(self, tracepath_tool):
+        """Test that the tool returns a comprehensive description"""
+        description = tracepath_tool.get_description()
+        assert "STATIC CALL PATH ANALYSIS" in description
+        assert "control flow" in description
+        assert "confidence levels" in description
+        assert "polymorphism" in description
+        assert "side effects" in description
+
+    def test_get_input_schema(self, tracepath_tool):
+        """Test that the input schema includes all required fields"""
+        schema = tracepath_tool.get_input_schema()
+
+        assert schema["type"] == "object"
+        assert "entry_point" in schema["properties"]
+        assert "files" in schema["properties"]
+
+        # Check required fields
+        required_fields = schema["required"]
+        assert "entry_point" in required_fields
+        assert "files" in required_fields
+
+        # Check optional parameters
+        assert "parameters" in schema["properties"]
+        assert "analysis_depth" in schema["properties"]
+        assert "language" in schema["properties"]
+        assert "confidence_threshold" in schema["properties"]
+
+        # Check enum values for analysis_depth
+        depth_enum = schema["properties"]["analysis_depth"]["enum"]
+        expected_depths = ["shallow", "medium", "deep"]
+        assert all(depth in depth_enum for depth in expected_depths)
+
+        # Check enum values for language
+        language_enum = schema["properties"]["language"]["enum"]
+        expected_languages = ["python", "javascript", "typescript", "csharp", "java"]
+        assert all(lang in language_enum for lang in expected_languages)
+
+    def test_get_model_category(self, tracepath_tool):
+        """Test that the tool uses extended reasoning category"""
+        category = tracepath_tool.get_model_category()
+        assert category == ToolModelCategory.EXTENDED_REASONING
+
+    def test_request_model_validation(self):
+        """Test request model validation"""
+        # Valid request
+        request = TracePathRequest(
+            entry_point="BookingManager::finalizeInvoice",
+            files=["/test/booking.py", "/test/payment.py"],
+            parameters={"invoice_id": 123, "payment_method": "credit_card"},
+            analysis_depth="medium",
+        )
+        assert request.entry_point == "BookingManager::finalizeInvoice"
+        assert len(request.files) == 2
+        assert request.analysis_depth == "medium"
+        assert request.confidence_threshold == 0.7  # default value
+
+        # Test validation with invalid confidence threshold
+        with pytest.raises(ValueError):
+            TracePathRequest(
+                entry_point="test::method", files=["/test/file.py"], confidence_threshold=1.5  # Invalid: > 1.0
+            )
+
+        # Invalid request (missing required fields)
+        with pytest.raises(ValueError):
+            TracePathRequest(files=["/test/file.py"])  # Missing entry_point
+
+    def test_language_detection_python(self, tracepath_tool):
+        """Test language detection for Python files"""
+        files = ["/test/booking.py", "/test/payment.py", "/test/utils.py"]
+        language = tracepath_tool.detect_primary_language(files)
+        assert language == "python"
+
+    def test_language_detection_javascript(self, tracepath_tool):
+        """Test language detection for JavaScript files"""
+        files = ["/test/app.js", "/test/component.jsx", "/test/utils.js"]
+        language = tracepath_tool.detect_primary_language(files)
+        assert language == "javascript"
+
+    def test_language_detection_typescript(self, tracepath_tool):
+        """Test language detection for TypeScript files"""
+        files = ["/test/app.ts", "/test/component.tsx", "/test/utils.ts"]
+        language = tracepath_tool.detect_primary_language(files)
+        assert language == "typescript"
+
+    def test_language_detection_csharp(self, tracepath_tool):
+        """Test language detection for C# files"""
+        files = ["/test/BookingService.cs", "/test/PaymentProcessor.cs"]
+        language = tracepath_tool.detect_primary_language(files)
+        assert language == "csharp"
+
+    def test_language_detection_java(self, tracepath_tool):
+        """Test language detection for Java files"""
+        files = ["/test/BookingManager.java", "/test/PaymentService.java"]
+        language = tracepath_tool.detect_primary_language(files)
+        assert language == "java"
+
+    def test_language_detection_mixed(self, tracepath_tool):
+        """Test language detection for mixed language files"""
+        files = ["/test/app.py", "/test/service.js", "/test/model.java"]
+        language = tracepath_tool.detect_primary_language(files)
+        assert language == "mixed"
+
+    def test_language_detection_unknown(self, tracepath_tool):
+        """Test language detection for unknown extensions"""
+        files = ["/test/config.xml", "/test/readme.txt"]
+        language = tracepath_tool.detect_primary_language(files)
+        assert language == "unknown"
+
+    def test_parse_entry_point_class_method_double_colon(self, tracepath_tool):
+        """Test parsing entry point with double colon syntax"""
+        result = tracepath_tool.parse_entry_point("BookingManager::finalizeInvoice", "python")
+
+        assert result["raw"] == "BookingManager::finalizeInvoice"
+        assert result["class_or_module"] == "BookingManager"
+        assert result["method_or_function"] == "finalizeInvoice"
+        assert result["type"] == "method"
+
+    def test_parse_entry_point_module_function_dot(self, tracepath_tool):
+        """Test parsing entry point with dot syntax"""
+        result = tracepath_tool.parse_entry_point("utils.validate_input", "python")
+
+        assert result["raw"] == "utils.validate_input"
+        assert result["class_or_module"] == "utils"
+        assert result["method_or_function"] == "validate_input"
+        assert result["type"] == "function"
+
+    def test_parse_entry_point_nested_module(self, tracepath_tool):
+        """Test parsing entry point with nested module syntax"""
+        result = tracepath_tool.parse_entry_point("payment.services.process_payment", "python")
+
+        assert result["raw"] == "payment.services.process_payment"
+        assert result["class_or_module"] == "payment.services"
+        assert result["method_or_function"] == "process_payment"
+        assert result["type"] == "function"
+
+    def test_parse_entry_point_function_only(self, tracepath_tool):
+        """Test parsing entry point with function name only"""
+        result = tracepath_tool.parse_entry_point("validate_payment", "python")
+
+        assert result["raw"] == "validate_payment"
+        assert result["class_or_module"] == ""
+        assert result["method_or_function"] == "validate_payment"
+        assert result["type"] == "function"
+
+    def test_parse_entry_point_camelcase_class(self, tracepath_tool):
+        """Test parsing entry point with CamelCase class (method detection)"""
+        result = tracepath_tool.parse_entry_point("PaymentProcessor.process", "java")
+
+        assert result["raw"] == "PaymentProcessor.process"
+        assert result["class_or_module"] == "PaymentProcessor"
+        assert result["method_or_function"] == "process"
+        assert result["type"] == "method"  # CamelCase suggests class method
+
+    @pytest.mark.asyncio
+    async def test_generate_structural_summary_phase1(self, tracepath_tool):
+        """Test structural summary generation (Phase 1 returns empty)"""
+        files = ["/test/booking.py", "/test/payment.py"]
+        summary = await tracepath_tool._generate_structural_summary(files, "python")
+
+        # Phase 1 implementation should return empty string
+        assert summary == ""
+
+    @pytest.mark.asyncio
+    async def test_prepare_prompt_basic(self, tracepath_tool):
+        """Test basic prompt preparation"""
+        request = TracePathRequest(
+            entry_point="BookingManager::finalizeInvoice",
+            files=["/test/booking.py"],
+            parameters={"invoice_id": 123},
+            analysis_depth="medium",
+        )
+
+        # Mock file content preparation
+        with patch.object(tracepath_tool, "_prepare_file_content_for_prompt") as mock_prep:
+            mock_prep.return_value = "def finalizeInvoice(self, invoice_id):\n    pass"
+            with patch.object(tracepath_tool, "_validate_token_limit"):
+                prompt = await tracepath_tool.prepare_prompt(request)
+
+        assert "ANALYSIS REQUEST" in prompt
+        assert "BookingManager::finalizeInvoice" in prompt
+        assert "medium" in prompt
+        assert "CODE TO ANALYZE" in prompt
+
+    @pytest.mark.asyncio
+    async def test_prepare_prompt_with_parameters(self, tracepath_tool):
+        """Test prompt preparation with parameter values"""
+        request = TracePathRequest(
+            entry_point="payment.process_payment",
+            files=["/test/payment.py"],
+            parameters={"amount": 100.50, "method": "credit_card"},
+            analysis_depth="deep",
+            include_db=True,
+            include_network=True,
+            include_fs=False,
+        )
+
+        with patch.object(tracepath_tool, "_prepare_file_content_for_prompt") as mock_prep:
+            mock_prep.return_value = "def process_payment(amount, method):\n    pass"
+            with patch.object(tracepath_tool, "_validate_token_limit"):
+                prompt = await tracepath_tool.prepare_prompt(request)
+
+        assert "Parameter Values: {'amount': 100.5, 'method': 'credit_card'}" in prompt
+        assert "Analysis Depth: deep" in prompt
+        assert "Include Side Effects: database, network" in prompt
+
+    @pytest.mark.asyncio
+    async def test_prepare_prompt_with_context(self, tracepath_tool):
+        """Test prompt preparation with additional context"""
+        request = TracePathRequest(
+            entry_point="UserService::authenticate",
+            files=["/test/auth.py"],
+            context="Focus on security implications and potential vulnerabilities",
+            focus_areas=["security", "error_handling"],
+        )
+
+        with patch.object(tracepath_tool, "_prepare_file_content_for_prompt") as mock_prep:
+            mock_prep.return_value = "def authenticate(self, username, password):\n    pass"
+            with patch.object(tracepath_tool, "_validate_token_limit"):
+                prompt = await tracepath_tool.prepare_prompt(request)
+
+        assert "Additional Context: Focus on security implications" in prompt
+        assert "Focus Areas: security, error_handling" in prompt
+
+    def test_format_response_markdown(self, tracepath_tool):
+        """Test response formatting for markdown output"""
+        request = TracePathRequest(
+            entry_point="BookingManager::finalizeInvoice", files=["/test/booking.py"], export_format="markdown"
+        )
+
+        response = "## Call Path Summary\n1. BookingManager::finalizeInvoice..."
+        model_info = {"model_response": Mock(friendly_name="Gemini Pro")}
+
+        formatted = tracepath_tool.format_response(response, request, model_info)
+
+        assert response in formatted
+        assert "Analysis Complete" in formatted
+        assert "Gemini Pro" in formatted
+        assert "confidence assessments" in formatted
+
+    def test_format_response_json(self, tracepath_tool):
+        """Test response formatting for JSON output"""
+        request = TracePathRequest(entry_point="payment.process", files=["/test/payment.py"], export_format="json")
+
+        response = '{"call_path": [...], "confidence": "high"}'
+
+        formatted = tracepath_tool.format_response(response, request)
+
+        assert response in formatted
+        assert "structured JSON analysis" in formatted
+        assert "confidence levels" in formatted
+
+    def test_format_response_plantuml(self, tracepath_tool):
+        """Test response formatting for PlantUML output"""
+        request = TracePathRequest(entry_point="service.execute", files=["/test/service.py"], export_format="plantuml")
+
+        response = "@startuml\nBooking -> Payment\n@enduml"
+
+        formatted = tracepath_tool.format_response(response, request)
+
+        assert response in formatted
+        assert "PlantUML diagram" in formatted
+        assert "Render the PlantUML" in formatted
+
+    def test_get_default_temperature(self, tracepath_tool):
+        """Test that the tool uses analytical temperature"""
+        from config import TEMPERATURE_ANALYTICAL
+
+        assert tracepath_tool.get_default_temperature() == TEMPERATURE_ANALYTICAL
+
+    def test_wants_line_numbers_by_default(self, tracepath_tool):
+        """Test that line numbers are enabled by default"""
+        # The base class should enable line numbers by default for precise references
+        # We test that this isn't overridden to disable them
+        assert hasattr(tracepath_tool, "wants_line_numbers_by_default")
+
+    def test_side_effects_configuration(self):
+        """Test side effects boolean configuration"""
+        request = TracePathRequest(
+            entry_point="test.function",
+            files=["/test/file.py"],
+            include_db=True,
+            include_network=False,
+            include_fs=True,
+        )
+
+        assert request.include_db is True
+        assert request.include_network is False
+        assert request.include_fs is True
+
+    def test_confidence_threshold_bounds(self):
+        """Test confidence threshold validation bounds"""
+        # Valid thresholds
+        request1 = TracePathRequest(entry_point="test.function", files=["/test/file.py"], confidence_threshold=0.0)
+        assert request1.confidence_threshold == 0.0
+
+        request2 = TracePathRequest(entry_point="test.function", files=["/test/file.py"], confidence_threshold=1.0)
+        assert request2.confidence_threshold == 1.0
+
+        # Invalid thresholds should raise ValidationError
+        with pytest.raises(ValueError):
+            TracePathRequest(entry_point="test.function", files=["/test/file.py"], confidence_threshold=-0.1)
+
+        with pytest.raises(ValueError):
+            TracePathRequest(entry_point="test.function", files=["/test/file.py"], confidence_threshold=1.1)
+
+    def test_signature_parameter(self):
+        """Test signature parameter for overload resolution"""
+        request = TracePathRequest(
+            entry_point="Calculator.add",
+            files=["/test/calc.cs"],
+            signature="public int Add(int a, int b)",
+            language="csharp",
+        )
+
+        assert request.signature == "public int Add(int a, int b)"
+        assert request.language == "csharp"
+
+    @pytest.mark.asyncio
+    async def test_prepare_prompt_with_language_override(self, tracepath_tool):
+        """Test prompt preparation with language override"""
+        request = TracePathRequest(
+            entry_point="Calculator::Add",
+            files=["/test/calc.py"],  # Python extension
+            language="csharp",  # Override to C#
+        )
+
+        with patch.object(tracepath_tool, "_prepare_file_content_for_prompt") as mock_prep:
+            mock_prep.return_value = "public class Calculator { }"
+            with patch.object(tracepath_tool, "_validate_token_limit"):
+                prompt = await tracepath_tool.prepare_prompt(request)
+
+        assert "Language: csharp" in prompt  # Should use override, not detected
+
+    def test_export_format_options(self):
+        """Test all export format options"""
+        formats = ["markdown", "json", "plantuml"]
+
+        for fmt in formats:
+            request = TracePathRequest(entry_point="test.function", files=["/test/file.py"], export_format=fmt)
+            assert request.export_format == fmt
+
+        # Invalid format should raise ValidationError
+        with pytest.raises(ValueError):
+            TracePathRequest(entry_point="test.function", files=["/test/file.py"], export_format="invalid_format")

tools/__init__.py

@@ -10,6 +10,7 @@ from .precommit import Precommit
 from .refactor import RefactorTool
 from .testgen import TestGenTool
 from .thinkdeep import ThinkDeepTool
+from .tracepath import TracePathTool
 
 __all__ = [
     "ThinkDeepTool",
@@ -20,4 +21,5 @@ __all__ = [
     "Precommit",
     "RefactorTool",
     "TestGenTool",
+    "TracePathTool",
 ]

tools/tracepath.py

@@ -0,0 +1,602 @@
+"""
+TracePath tool - Static call path prediction and control flow analysis
+
+This tool analyzes code to predict and explain full call paths and control flow without executing code.
+Given a method name, its owning class/module, and parameter combinations or runtime values, it predicts
+the complete chain of method/function calls that would be triggered.
+
+Key Features:
+- Static call path prediction with confidence levels
+- Polymorphism and dynamic dispatch analysis
+- Value-driven flow analysis based on parameter combinations
+- Side effects identification (database, network, filesystem)
+- Branching analysis for conditional logic
+- Hybrid AI-first approach with optional AST preprocessing for enhanced accuracy
+"""
+
+import logging
+import os
+import re
+from typing import Any, Literal, Optional
+
+from pydantic import Field
+
+from config import TEMPERATURE_ANALYTICAL
+from systemprompts import TRACEPATH_PROMPT
+
+from .base import BaseTool, ToolRequest
+
+logger = logging.getLogger(__name__)
+
+
+class TracePathRequest(ToolRequest):
+    """
+    Request model for the tracepath tool.
+
+    This model defines all parameters for customizing the call path analysis process.
+    """
+
+    entry_point: str = Field(
+        ...,
+        description="Method/function to trace (e.g., 'BookingManager::finalizeInvoice', 'utils.validate_input')",
+    )
+    files: list[str] = Field(
+        ...,
+        description="Code files or directories to analyze (must be absolute paths)",
+    )
+    parameters: Optional[dict[str, Any]] = Field(
+        None,
+        description="Parameter values to analyze - format: {param_name: value_or_type}",
+    )
+    context: Optional[str] = Field(
+        None,
+        description="Additional context about analysis goals or specific scenarios to focus on",
+    )
+    analysis_depth: Literal["shallow", "medium", "deep"] = Field(
+        "medium",
+        description="Analysis depth: shallow (direct calls), medium (2-3 levels), deep (full trace)",
+    )
+    language: Optional[str] = Field(
+        None,
+        description="Override auto-detection: python, javascript, typescript, csharp, java",
+    )
+    signature: Optional[str] = Field(
+        None,
+        description="Fully-qualified signature for overload resolution in languages like C#/Java",
+    )
+    confidence_threshold: Optional[float] = Field(
+        0.7,
+        description="Filter speculative branches (0-1, default 0.7)",
+        ge=0.0,
+        le=1.0,
+    )
+    include_db: bool = Field(
+        True,
+        description="Include database interactions in side effects analysis",
+    )
+    include_network: bool = Field(
+        True,
+        description="Include network calls in side effects analysis",
+    )
+    include_fs: bool = Field(
+        True,
+        description="Include filesystem operations in side effects analysis",
+    )
+    export_format: Literal["markdown", "json", "plantuml"] = Field(
+        "markdown",
+        description="Output format for the analysis results",
+    )
+    focus_areas: Optional[list[str]] = Field(
+        None,
+        description="Specific aspects to focus on (e.g., 'performance', 'security', 'error_handling')",
+    )
+
+
+class TracePathTool(BaseTool):
+    """
+    TracePath tool implementation.
+
+    This tool analyzes code to predict static call paths and control flow without execution.
+    Uses a hybrid AI-first approach with optional AST preprocessing for enhanced accuracy.
+    """
+
+    def get_name(self) -> str:
+        return "tracepath"
+
+    def get_description(self) -> str:
+        return (
+            "STATIC CALL PATH ANALYSIS - Predicts and explains full call paths and control flow without executing code. "
+            "Given a method/function name and parameter values, traces the complete execution path including "
+            "conditional branches, polymorphism resolution, and side effects. "
+            "Perfect for: understanding complex code flows, impact analysis, debugging assistance, architecture review. "
+            "Provides confidence levels for predictions and identifies uncertain calls due to dynamic behavior. "
+            "Choose thinking_mode based on code complexity: 'low' for simple functions, "
+            "'medium' for standard analysis (default), 'high' for complex systems, "
+            "'max' for legacy codebases requiring deep analysis. "
+            "Note: If you're not currently using a top-tier model such as Opus 4 or above, these tools can provide enhanced capabilities."
+        )
+
+    def get_input_schema(self) -> dict[str, Any]:
+        schema = {
+            "type": "object",
+            "properties": {
+                "entry_point": {
+                    "type": "string",
+                    "description": "Method/function to trace (e.g., 'BookingManager::finalizeInvoice', 'utils.validate_input')",
+                },
+                "files": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Code files or directories to analyze (must be absolute paths)",
+                },
+                "model": self.get_model_field_schema(),
+                "parameters": {
+                    "type": "object",
+                    "description": "Parameter values to analyze - format: {param_name: value_or_type}",
+                },
+                "context": {
+                    "type": "string",
+                    "description": "Additional context about analysis goals or specific scenarios to focus on",
+                },
+                "analysis_depth": {
+                    "type": "string",
+                    "enum": ["shallow", "medium", "deep"],
+                    "default": "medium",
+                    "description": "Analysis depth: shallow (direct calls), medium (2-3 levels), deep (full trace)",
+                },
+                "language": {
+                    "type": "string",
+                    "enum": ["python", "javascript", "typescript", "csharp", "java"],
+                    "description": "Override auto-detection for programming language",
+                },
+                "signature": {
+                    "type": "string",
+                    "description": "Fully-qualified signature for overload resolution",
+                },
+                "confidence_threshold": {
+                    "type": "number",
+                    "minimum": 0.0,
+                    "maximum": 1.0,
+                    "default": 0.7,
+                    "description": "Filter speculative branches (0-1)",
+                },
+                "include_db": {
+                    "type": "boolean",
+                    "default": True,
+                    "description": "Include database interactions in analysis",
+                },
+                "include_network": {
+                    "type": "boolean",
+                    "default": True,
+                    "description": "Include network calls in analysis",
+                },
+                "include_fs": {
+                    "type": "boolean",
+                    "default": True,
+                    "description": "Include filesystem operations in analysis",
+                },
+                "export_format": {
+                    "type": "string",
+                    "enum": ["markdown", "json", "plantuml"],
+                    "default": "markdown",
+                    "description": "Output format for analysis results",
+                },
+                "focus_areas": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Specific aspects to focus on",
+                },
+                "temperature": {
+                    "type": "number",
+                    "description": "Temperature (0-1, default 0.2 for analytical precision)",
+                    "minimum": 0,
+                    "maximum": 1,
+                },
+                "thinking_mode": {
+                    "type": "string",
+                    "enum": ["minimal", "low", "medium", "high", "max"],
+                    "description": "Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max)",
+                },
+                "use_websearch": {
+                    "type": "boolean",
+                    "description": "Enable web search for framework documentation and patterns",
+                    "default": True,
+                },
+                "continuation_id": {
+                    "type": "string",
+                    "description": "Thread continuation ID for multi-turn conversations across tools",
+                },
+            },
+            "required": ["entry_point", "files"] + (["model"] if self.is_effective_auto_mode() else []),
+        }
+
+        return schema
+
+    def get_system_prompt(self) -> str:
+        return TRACEPATH_PROMPT
+
+    def get_default_temperature(self) -> float:
+        return TEMPERATURE_ANALYTICAL
+
+    # Line numbers are enabled by default for precise code references
+
+    def get_model_category(self):
+        """TracePath requires extended reasoning for complex flow analysis"""
+        from tools.models import ToolModelCategory
+
+        return ToolModelCategory.EXTENDED_REASONING
+
+    def get_request_model(self):
+        return TracePathRequest
+
+    def detect_primary_language(self, file_paths: list[str]) -> str:
+        """
+        Detect the primary programming language from file extensions.
+
+        Args:
+            file_paths: List of file paths to analyze
+
+        Returns:
+            str: Detected language or "mixed" if multiple languages found
+        """
+        # Language detection based on file extensions
+        language_extensions = {
+            "python": {".py", ".pyx", ".pyi"},
+            "javascript": {".js", ".jsx", ".mjs", ".cjs"},
+            "typescript": {".ts", ".tsx", ".mts", ".cts"},
+            "java": {".java"},
+            "csharp": {".cs"},
+            "cpp": {".cpp", ".cc", ".cxx", ".c", ".h", ".hpp"},
+            "go": {".go"},
+            "rust": {".rs"},
+            "swift": {".swift"},
+            "kotlin": {".kt", ".kts"},
+            "ruby": {".rb"},
+            "php": {".php"},
+            "scala": {".scala"},
+        }
+
+        # Count files by language
+        language_counts = {}
+        for file_path in file_paths:
+            extension = os.path.splitext(file_path.lower())[1]
+            for lang, exts in language_extensions.items():
+                if extension in exts:
+                    language_counts[lang] = language_counts.get(lang, 0) + 1
+                    break
+
+        if not language_counts:
+            return "unknown"
+
+        # Return most common language, or "mixed" if multiple languages
+        max_count = max(language_counts.values())
+        dominant_languages = [lang for lang, count in language_counts.items() if count == max_count]
+
+        if len(dominant_languages) == 1:
+            return dominant_languages[0]
+        else:
+            return "mixed"
+
+    def parse_entry_point(self, entry_point: str, language: str) -> dict[str, str]:
+        """
+        Parse entry point string to extract class/module and method/function information.
+
+        Args:
+            entry_point: Entry point string (e.g., "BookingManager::finalizeInvoice", "utils.validate_input")
+            language: Detected or specified programming language
+
+        Returns:
+            dict: Parsed entry point information
+        """
+        result = {
+            "raw": entry_point,
+            "class_or_module": "",
+            "method_or_function": "",
+            "type": "unknown",
+        }
+
+        # Common patterns across languages
+        patterns = {
+            # Class::method (C++, PHP style)
+            "class_method_double_colon": r"^([A-Za-z_][A-Za-z0-9_]*?)::([A-Za-z_][A-Za-z0-9_]*?)$",
+            # Module.function or Class.method (Python, JavaScript, etc.)
+            "module_function_dot": r"^([A-Za-z_][A-Za-z0-9_]*?)\.([A-Za-z_][A-Za-z0-9_]*?)$",
+            # Nested module.submodule.function
+            "nested_module_dot": r"^([A-Za-z_][A-Za-z0-9_.]*?)\.([A-Za-z_][A-Za-z0-9_]*?)$",
+            # Just function name
+            "function_only": r"^([A-Za-z_][A-Za-z0-9_]*?)$",
+        }
+
+        # Try patterns in order of specificity
+        for pattern_name, pattern in patterns.items():
+            match = re.match(pattern, entry_point.strip())
+            if match:
+                if pattern_name == "function_only":
+                    result["method_or_function"] = match.group(1)
+                    result["type"] = "function"
+                else:
+                    result["class_or_module"] = match.group(1)
+                    result["method_or_function"] = match.group(2)
+
+                    # Determine if it's a class method or module function based on naming conventions
+                    if pattern_name == "class_method_double_colon":
+                        result["type"] = "method"
+                    elif result["class_or_module"][0].isupper():
+                        result["type"] = "method"  # Likely class method (CamelCase)
+                    else:
+                        result["type"] = "function"  # Likely module function (snake_case)
+                break
+
+        logger.debug(f"[TRACEPATH] Parsed entry point '{entry_point}' as: {result}")
+        return result
+
+    async def _generate_structural_summary(self, files: list[str], language: str) -> str:
+        """
+        Generate structural summary of the code using AST parsing.
+
+        Phase 1: Returns empty string (pure AI-driven approach)
+        Phase 2: Will contain language-specific AST parsing logic
+
+        Args:
+            files: List of file paths to analyze
+            language: Detected programming language
+
+        Returns:
+            str: Structural summary or empty string for Phase 1
+        """
+        # Phase 1 implementation: Pure AI-driven approach
+        # Phase 2 will add AST parsing for enhanced context
+
+        if language == "python":
+            # Placeholder for Python AST parsing using built-in 'ast' module
+            # Will extract class definitions, method signatures, and direct calls
+            pass
+        elif language in ["javascript", "typescript"]:
+            # Placeholder for JavaScript/TypeScript parsing using acorn or TS compiler API
+            pass
+        elif language == "csharp":
+            # Placeholder for C# parsing using Microsoft Roslyn SDK
+            pass
+        elif language == "java":
+            # Placeholder for Java parsing (future implementation)
+            pass
+
+        # For Phase 1, return empty to rely on pure LLM analysis
+        logger.debug(f"[TRACEPATH] Phase 1: No structural summary generated for {language}")
+        return ""
+
+    async def prepare_prompt(self, request: TracePathRequest) -> str:
+        """
+        Prepare the complete prompt for call path analysis.
+
+        This method combines:
+        - System prompt with analysis instructions
+        - User context and entry point information
+        - File contents with line numbers
+        - Structural summary (Phase 2)
+        - Analysis parameters and constraints
+
+        Args:
+            request: The validated tracepath request
+
+        Returns:
+            str: Complete prompt for the model
+
+        Raises:
+            ValueError: If the prompt exceeds token limits
+        """
+        logger.info(
+            f"[TRACEPATH] Preparing prompt for entry point '{request.entry_point}' with {len(request.files)} files"
+        )
+        logger.debug(f"[TRACEPATH] Analysis depth: {request.analysis_depth}, Export format: {request.export_format}")
+
+        # Check for prompt.txt in files
+        prompt_content, updated_files = self.handle_prompt_file(request.files)
+
+        # If prompt.txt was found, incorporate it into the context
+        if prompt_content:
+            logger.debug("[TRACEPATH] Found prompt.txt file, incorporating content")
+            if request.context:
+                request.context = prompt_content + "\n\n" + request.context
+            else:
+                request.context = prompt_content
+
+        # Update request files list
+        if updated_files is not None:
+            logger.debug(f"[TRACEPATH] Updated files list after prompt.txt processing: {len(updated_files)} files")
+            request.files = updated_files
+
+        # Check user input size at MCP transport boundary (before adding internal content)
+        if request.context:
+            size_check = self.check_prompt_size(request.context)
+            if size_check:
+                from tools.models import ToolOutput
+
+                raise ValueError(f"MCP_SIZE_CHECK:{ToolOutput(**size_check).model_dump_json()}")
+
+        # Detect or use specified language
+        if request.language:
+            primary_language = request.language
+            logger.debug(f"[TRACEPATH] Using specified language: {primary_language}")
+        else:
+            primary_language = self.detect_primary_language(request.files)
+            logger.debug(f"[TRACEPATH] Detected primary language: {primary_language}")
+
+        # Parse entry point
+        entry_point_info = self.parse_entry_point(request.entry_point, primary_language)
+        logger.debug(f"[TRACEPATH] Entry point parsed as: {entry_point_info}")
+
+        # Generate structural summary (Phase 1: returns empty, Phase 2: AST analysis)
+        continuation_id = getattr(request, "continuation_id", None)
+        structural_summary = await self._generate_structural_summary(request.files, primary_language)
+
+        # Use centralized file processing logic for main code files (with line numbers enabled)
+        logger.debug(f"[TRACEPATH] Preparing {len(request.files)} code files for analysis")
+        code_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Code to analyze")
+
+        if code_content:
+            from utils.token_utils import estimate_tokens
+
+            code_tokens = estimate_tokens(code_content)
+            logger.info(f"[TRACEPATH] Code files embedded successfully: {code_tokens:,} tokens")
+        else:
+            logger.warning("[TRACEPATH] No code content after file processing")
+
+        # Build the complete prompt
+        prompt_parts = []
+
+        # Add system prompt
+        prompt_parts.append(self.get_system_prompt())
+
+        # Add structural summary if available (Phase 2)
+        if structural_summary:
+            prompt_parts.append("\n=== STRUCTURAL SUMMARY ===")
+            prompt_parts.append(structural_summary)
+            prompt_parts.append("=== END STRUCTURAL SUMMARY ===")
+
+        # Add user context and analysis parameters
+        prompt_parts.append("\n=== ANALYSIS REQUEST ===")
+        prompt_parts.append(f"Entry Point: {request.entry_point}")
+        if entry_point_info["type"] != "unknown":
+            prompt_parts.append(
+                f"Parsed as: {entry_point_info['type']} '{entry_point_info['method_or_function']}' in {entry_point_info['class_or_module'] or 'global scope'}"
+            )
+
+        prompt_parts.append(f"Language: {primary_language}")
+        prompt_parts.append(f"Analysis Depth: {request.analysis_depth}")
+        prompt_parts.append(f"Confidence Threshold: {request.confidence_threshold}")
+
+        if request.signature:
+            prompt_parts.append(f"Method Signature: {request.signature}")
+
+        if request.parameters:
+            prompt_parts.append(f"Parameter Values: {request.parameters}")
+
+        # Side effects configuration
+        side_effects = []
+        if request.include_db:
+            side_effects.append("database")
+        if request.include_network:
+            side_effects.append("network")
+        if request.include_fs:
+            side_effects.append("filesystem")
+        if side_effects:
+            prompt_parts.append(f"Include Side Effects: {', '.join(side_effects)}")
+
+        if request.focus_areas:
+            prompt_parts.append(f"Focus Areas: {', '.join(request.focus_areas)}")
+
+        if request.context:
+            prompt_parts.append(f"Additional Context: {request.context}")
+
+        prompt_parts.append(f"Export Format: {request.export_format}")
+        prompt_parts.append("=== END REQUEST ===")
+
+        # Add web search instruction if enabled
+        websearch_instruction = self.get_websearch_instruction(
+            request.use_websearch,
+            f"""When analyzing call paths for {primary_language} code, consider if searches for these would help:
+- Framework-specific call patterns and lifecycle methods
+- Language-specific dispatch mechanisms and polymorphism
+- Common side-effect patterns for libraries used in the code
+- Documentation for external APIs and services called
+- Known design patterns that affect call flow""",
+        )
+        if websearch_instruction:
+            prompt_parts.append(websearch_instruction)
+
+        # Add main code to analyze
+        prompt_parts.append("\n=== CODE TO ANALYZE ===")
+        prompt_parts.append(code_content)
+        prompt_parts.append("=== END CODE ===")
+
+        # Add analysis instructions
+        analysis_instructions = [
+            f"\nPlease perform a {request.analysis_depth} static call path analysis for the entry point '{request.entry_point}'."
+        ]
+
+        if request.parameters:
+            analysis_instructions.append(
+                "Pay special attention to how the provided parameter values affect the execution flow."
+            )
+
+        if request.confidence_threshold < 1.0:
+            analysis_instructions.append(
+                f"Filter out speculative paths with confidence below {request.confidence_threshold}."
+            )
+
+        analysis_instructions.append(f"Format the output as {request.export_format}.")
+
+        prompt_parts.extend(analysis_instructions)
+
+        full_prompt = "\n".join(prompt_parts)
+
+        # Log final prompt statistics
+        from utils.token_utils import estimate_tokens
+
+        total_tokens = estimate_tokens(full_prompt)
+        logger.info(f"[TRACEPATH] Complete prompt prepared: {total_tokens:,} tokens, {len(full_prompt):,} characters")
+
+        return full_prompt
+
+    def format_response(self, response: str, request: TracePathRequest, model_info: Optional[dict] = None) -> str:
+        """
+        Format the call path analysis response.
+
+        The base tool handles structured response validation via SPECIAL_STATUS_MODELS,
+        so this method focuses on providing clear guidance for next steps.
+
+        Args:
+            response: The raw analysis from the model
+            request: The original request for context
+            model_info: Optional dict with model metadata
+
+        Returns:
+            str: The response with additional guidance
+        """
+        logger.debug(f"[TRACEPATH] Formatting response for entry point '{request.entry_point}'")
+
+        # Get the friendly model name
+        model_name = "the model"
+        if model_info and model_info.get("model_response"):
+            model_name = model_info["model_response"].friendly_name or "the model"
+
+        # Add contextual footer based on analysis depth and format
+        if request.export_format == "json":
+            footer = f"""
+---
+
+**Analysis Complete**: {model_name} has provided a structured JSON analysis of the call path for `{request.entry_point}`.
+
+**Next Steps**:
+- Review the confidence levels for each predicted call
+- Investigate any uncertain calls marked with low confidence
+- Use this analysis for impact assessment, debugging, or architecture review
+- For deeper analysis, increase analysis_depth to 'deep' or provide additional context files
+"""
+        elif request.export_format == "plantuml":
+            footer = f"""
+---
+
+**Analysis Complete**: {model_name} has generated a PlantUML diagram showing the call path for `{request.entry_point}`.
+
+**Next Steps**:
+- Render the PlantUML diagram to visualize the call flow
+- Review branching points and conditional logic
+- Verify the predicted paths against your understanding of the code
+- Use this for documentation or architectural discussions
+"""
+        else:  # markdown
+            footer = f"""
+---
+
+**Analysis Complete**: {model_name} has traced the execution path for `{request.entry_point}` at {request.analysis_depth} depth.
+
+**Next Steps**:
+- Review the call path summary and confidence assessments
+- Pay attention to uncertain calls that may require runtime verification
+- Use the code anchors (file:line references) to navigate to critical decision points
+- Consider this analysis for debugging, impact assessment, or refactoring decisions
+"""
+
+        return f"{response}{footer}"