my-pal-mcp-server/tools/thinkdeep.py

"""
ThinkDeep tool - Extended reasoning and problem-solving
"""

from typing import TYPE_CHECKING, Any, Optional

from mcp.types import TextContent
from pydantic import Field

if TYPE_CHECKING:
    from tools.models import ToolModelCategory

from config import TEMPERATURE_CREATIVE
from prompts import THINKDEEP_PROMPT

from .base import BaseTool, ToolRequest
from .models import ToolOutput


class ThinkDeepRequest(ToolRequest):
    """Request model for thinkdeep tool"""

    prompt: str = Field(..., description="Your current thinking/analysis to extend and validate")
    problem_context: Optional[str] = Field(None, description="Additional context about the problem or goal")
    focus_areas: Optional[list[str]] = Field(
        None,
        description="Specific aspects to focus on (architecture, performance, security, etc.)",
    )
    files: Optional[list[str]] = Field(
        None,
        description="Optional file paths or directories for additional context (must be absolute paths)",
    )


class ThinkDeepTool(BaseTool):
    """Extended thinking and reasoning tool"""

    def get_name(self) -> str:
        return "thinkdeep"

    def get_description(self) -> str:
        return (
            "EXTENDED THINKING & REASONING - Your deep thinking partner for complex problems. "
            "Use this when you need to think deeper about a problem, extend your analysis, explore alternatives, or validate approaches. "
            "Perfect for: architecture decisions, complex bugs, performance challenges, security analysis. "
            "I'll challenge assumptions, find edge cases, and provide alternative solutions. "
            "IMPORTANT: Choose the appropriate thinking_mode based on task complexity - "
            "'low' for quick analysis, 'medium' for standard problems, 'high' for complex issues (default), "
            "'max' for extremely complex challenges requiring deepest analysis. "
            "When in doubt, err on the side of a higher mode for truly deep thought and evaluation."
        )

    def get_input_schema(self) -> dict[str, Any]:
        schema = {
            "type": "object",
            "properties": {
                "prompt": {
                    "type": "string",
                    "description": "Your current thinking/analysis to extend and validate",
                },
                "model": self.get_model_field_schema(),
                "problem_context": {
                    "type": "string",
                    "description": "Additional context about the problem or goal",
                },
                "focus_areas": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Specific aspects to focus on (architecture, performance, security, etc.)",
                },
                "files": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Optional file paths or directories for additional context (must be absolute paths)",
                },
                "temperature": {
                    "type": "number",
                    "description": "Temperature for creative thinking (0-1, default 0.7)",
                    "minimum": 0,
                    "maximum": 1,
                },
                "thinking_mode": {
                    "type": "string",
                    "enum": ["minimal", "low", "medium", "high", "max"],
                    "description": f"Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max). Defaults to '{self.get_default_thinking_mode()}' if not specified.",
                },
                "use_websearch": {
                    "type": "boolean",
                    "description": "Enable web search for documentation, best practices, and current information. Particularly useful for: brainstorming sessions, architectural design discussions, exploring industry best practices, working with specific frameworks/technologies, researching solutions to complex problems, or when current documentation and community insights would enhance the analysis.",
                    "default": True,
                },
                "continuation_id": {
                    "type": "string",
                    "description": "Thread continuation ID for multi-turn conversations. Can be used to continue conversations across different tools. Only provide this if continuing a previous conversation thread.",
                },
            },
            "required": ["prompt"] + (["model"] if self.is_effective_auto_mode() else []),
        }

        return schema

    def get_system_prompt(self) -> str:
        return THINKDEEP_PROMPT

    def get_default_temperature(self) -> float:
        return TEMPERATURE_CREATIVE

    def get_default_thinking_mode(self) -> str:
        """ThinkDeep uses configurable thinking mode, defaults to high"""
        from config import DEFAULT_THINKING_MODE_THINKDEEP

        return DEFAULT_THINKING_MODE_THINKDEEP

    def get_model_category(self) -> "ToolModelCategory":
        """ThinkDeep requires extended reasoning capabilities"""
        from tools.models import ToolModelCategory

        return ToolModelCategory.EXTENDED_REASONING

    def get_request_model(self):
        return ThinkDeepRequest

    async def execute(self, arguments: dict[str, Any]) -> list[TextContent]:
        """Override execute to check current_analysis size before processing"""
        # First validate request
        request_model = self.get_request_model()
        request = request_model(**arguments)

        # Check prompt size
        size_check = self.check_prompt_size(request.prompt)
        if size_check:
            return [TextContent(type="text", text=ToolOutput(**size_check).model_dump_json())]

        # Continue with normal execution
        return await super().execute(arguments)

    async def prepare_prompt(self, request: ThinkDeepRequest) -> str:
        """Prepare the full prompt for extended thinking"""
        # Check for prompt.txt in files
        prompt_content, updated_files = self.handle_prompt_file(request.files)

        # Use prompt.txt content if available, otherwise use the prompt field
        current_analysis = prompt_content if prompt_content else request.prompt

        # Update request files list
        if updated_files is not None:
            request.files = updated_files

        # Build context parts
        context_parts = [f"=== CLAUDE'S CURRENT ANALYSIS ===\n{current_analysis}\n=== END ANALYSIS ==="]

        if request.problem_context:
            context_parts.append(f"\n=== PROBLEM CONTEXT ===\n{request.problem_context}\n=== END CONTEXT ===")

        # Add reference files if provided
        if request.files:
            # Use centralized file processing logic
            continuation_id = getattr(request, "continuation_id", None)
            file_content = self._prepare_file_content_for_prompt(request.files, continuation_id, "Reference files")

            if file_content:
                context_parts.append(f"\n=== REFERENCE FILES ===\n{file_content}\n=== END FILES ===")

        full_context = "\n".join(context_parts)

        # Check token limits
        self._validate_token_limit(full_context, "Context")

        # Add focus areas instruction if specified
        focus_instruction = ""
        if request.focus_areas:
            areas = ", ".join(request.focus_areas)
            focus_instruction = f"\n\nFOCUS AREAS: Please pay special attention to {areas} aspects."

        # Add web search instruction if enabled
        websearch_instruction = self.get_websearch_instruction(
            request.use_websearch,
            """When analyzing complex problems, consider if searches for these would help:
- Current documentation for specific technologies, frameworks, or APIs mentioned
- Known issues, workarounds, or community solutions for similar problems
- Recent updates, deprecations, or best practices that might affect the approach
- Official sources to verify assumptions or clarify technical details""",
        )

        # Combine system prompt with context
        full_prompt = f"""{self.get_system_prompt()}{focus_instruction}{websearch_instruction}

{full_context}

Please provide deep analysis that extends Claude's thinking with:
1. Alternative approaches and solutions
2. Edge cases and potential failure modes
3. Critical evaluation of assumptions
4. Concrete implementation suggestions
5. Risk assessment and mitigation strategies"""

        return full_prompt

    def format_response(self, response: str, request: ThinkDeepRequest, model_info: Optional[dict] = None) -> str:
        """Format the response with clear attribution and critical thinking prompt"""
        # Get the friendly model name
        model_name = "your fellow developer"
        if model_info and model_info.get("model_response"):
            model_name = model_info["model_response"].friendly_name or "your fellow developer"

        return f"""{response}

---

## Critical Evaluation Required

Claude, please critically evaluate {model_name}'s analysis by thinking hard about the following:

1. **Technical merit** - Which suggestions are valuable vs. have limitations?
2. **Constraints** - Fit with codebase patterns, performance, security, architecture
3. **Risks** - Hidden complexities, edge cases, potential failure modes
4. **Final recommendation** - Synthesize both perspectives, then think deeply further to explore additional considerations and arrive at the best technical solution

Remember: Use {model_name}'s insights to enhance, not replace, your analysis."""