refactor: rename think_deeper to thinkdeep for brevity
- Renamed `think_deeper` tool to `thinkdeep` for shorter, cleaner naming - Updated all imports from ThinkDeeperTool to ThinkDeepTool - Updated all references from THINK_DEEPER_PROMPT to THINKDEEP_PROMPT - Updated tool registration in server.py - Updated all test files to use new naming convention - Updated README documentation to reflect new tool names - All functionality remains the same, only naming has changed This completes the tool renaming refactor for improved clarity and consistency. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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Fahad
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tools/thinkdeep.py
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tools/thinkdeep.py
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"""
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ThinkDeep tool - Extended reasoning and problem-solving
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"""
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from typing import Any, Optional
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from mcp.types import TextContent
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from pydantic import Field
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from config import TEMPERATURE_CREATIVE
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from prompts import THINKDEEP_PROMPT
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from utils import read_files
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from .base import BaseTool, ToolRequest
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from .models import ToolOutput
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class ThinkDeepRequest(ToolRequest):
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"""Request model for thinkdeep tool"""
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current_analysis: str = Field(..., description="Claude's current thinking/analysis to extend")
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problem_context: Optional[str] = Field(None, description="Additional context about the problem or goal")
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focus_areas: Optional[list[str]] = Field(
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None,
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description="Specific aspects to focus on (architecture, performance, security, etc.)",
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)
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files: Optional[list[str]] = Field(
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None,
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description="Optional file paths or directories for additional context (must be absolute paths)",
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)
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class ThinkDeepTool(BaseTool):
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"""Extended thinking and reasoning tool"""
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def get_name(self) -> str:
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return "thinkdeep"
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def get_description(self) -> str:
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return (
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"EXTENDED THINKING & REASONING - Your deep thinking partner for complex problems. "
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"Use this when you need to think deeper about a problem, extend your analysis, explore alternatives, or validate approaches. "
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"Perfect for: architecture decisions, complex bugs, performance challenges, security analysis. "
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"I'll challenge assumptions, find edge cases, and provide alternative solutions. "
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"IMPORTANT: Choose the appropriate thinking_mode based on task complexity - "
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"'low' for quick analysis, 'medium' for standard problems, 'high' for complex issues (default), "
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"'max' for extremely complex challenges requiring deepest analysis. "
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"When in doubt, err on the side of a higher mode for truly deep thought and evaluation."
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)
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def get_input_schema(self) -> dict[str, Any]:
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return {
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"type": "object",
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"properties": {
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"current_analysis": {
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"type": "string",
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"description": "Your current thinking/analysis to extend and validate",
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},
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"problem_context": {
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"type": "string",
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"description": "Additional context about the problem or goal",
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},
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"focus_areas": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Specific aspects to focus on (architecture, performance, security, etc.)",
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},
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"files": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional file paths or directories for additional context (must be absolute paths)",
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},
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"temperature": {
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"type": "number",
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"description": "Temperature for creative thinking (0-1, default 0.7)",
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"minimum": 0,
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"maximum": 1,
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},
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"thinking_mode": {
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"type": "string",
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"enum": ["minimal", "low", "medium", "high", "max"],
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"description": "Thinking depth: minimal (128), low (2048), medium (8192), high (16384), max (32768)",
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"default": "high",
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},
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"use_websearch": {
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"type": "boolean",
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"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.",
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"default": False,
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},
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},
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"required": ["current_analysis"],
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}
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def get_system_prompt(self) -> str:
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return THINKDEEP_PROMPT
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def get_default_temperature(self) -> float:
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return TEMPERATURE_CREATIVE
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def get_default_thinking_mode(self) -> str:
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"""ThinkDeep uses high thinking by default"""
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return "high"
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def get_request_model(self):
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return ThinkDeepRequest
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async def execute(self, arguments: dict[str, Any]) -> list[TextContent]:
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"""Override execute to check current_analysis size before processing"""
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# First validate request
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request_model = self.get_request_model()
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request = request_model(**arguments)
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# Check current_analysis size
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size_check = self.check_prompt_size(request.current_analysis)
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if size_check:
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return [TextContent(type="text", text=ToolOutput(**size_check).model_dump_json())]
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# Continue with normal execution
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return await super().execute(arguments)
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async def prepare_prompt(self, request: ThinkDeepRequest) -> str:
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"""Prepare the full prompt for extended thinking"""
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# Check for prompt.txt in files
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prompt_content, updated_files = self.handle_prompt_file(request.files)
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# Use prompt.txt content if available, otherwise use the current_analysis field
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current_analysis = prompt_content if prompt_content else request.current_analysis
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# Update request files list
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if updated_files is not None:
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request.files = updated_files
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# Build context parts
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context_parts = [f"=== CLAUDE'S CURRENT ANALYSIS ===\n{current_analysis}\n=== END ANALYSIS ==="]
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if request.problem_context:
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context_parts.append(f"\n=== PROBLEM CONTEXT ===\n{request.problem_context}\n=== END CONTEXT ===")
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# Add reference files if provided
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if request.files:
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file_content = read_files(request.files)
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context_parts.append(f"\n=== REFERENCE FILES ===\n{file_content}\n=== END FILES ===")
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full_context = "\n".join(context_parts)
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# Check token limits
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self._validate_token_limit(full_context, "Context")
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# Add focus areas instruction if specified
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focus_instruction = ""
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if request.focus_areas:
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areas = ", ".join(request.focus_areas)
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focus_instruction = f"\n\nFOCUS AREAS: Please pay special attention to {areas} aspects."
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# Add web search instruction if enabled
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websearch_instruction = self.get_websearch_instruction(
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request.use_websearch,
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"""Specifically search for:
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- Current documentation for technologies, frameworks, or APIs being discussed
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- Similar issues and their solutions from the community
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- Best practices and recent developments
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- Official sources to verify information""",
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)
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# Combine system prompt with context
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full_prompt = f"""{self.get_system_prompt()}{focus_instruction}{websearch_instruction}
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{full_context}
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Please provide deep analysis that extends Claude's thinking with:
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1. Alternative approaches and solutions
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2. Edge cases and potential failure modes
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3. Critical evaluation of assumptions
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4. Concrete implementation suggestions
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5. Risk assessment and mitigation strategies"""
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return full_prompt
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def format_response(self, response: str, request: ThinkDeepRequest) -> str:
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"""Format the response with clear attribution and critical thinking prompt"""
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return f"""## Extended Analysis by Gemini
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{response}
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---
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## Critical Evaluation Required
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Claude, please critically evaluate Gemini's analysis by considering:
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1. **Technical merit** - Which suggestions are valuable vs. have limitations?
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2. **Constraints** - Fit with codebase patterns, performance, security, architecture
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3. **Risks** - Hidden complexities, edge cases, potential failure modes
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4. **Final recommendation** - Synthesize both perspectives, then think deeply further to explore additional considerations and arrive at the best technical solution
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Remember: Use Gemini's insights to enhance, not replace, your analysis."""
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