feat: complete redesign to v2.4.0 - Claude's ultimate development partner
Major redesign of Gemini MCP Server with modular architecture: - Removed all emoji characters from tool outputs for clean terminal display - Kept review category emojis (🔴🟠🟡🟢) per user preference - Added 4 specialized tools: - think_deeper: Extended reasoning and problem-solving (temp 0.7) - review_code: Professional code review with severity levels (temp 0.2) - debug_issue: Root cause analysis and debugging (temp 0.2) - analyze: General-purpose file analysis (temp 0.2) - Modular architecture with base tool class and Pydantic models - Verbose tool descriptions with natural language triggers - Updated README with comprehensive examples and real-world use cases - All 25 tests passing, type checking clean, critical linting clean BREAKING CHANGE: Removed analyze_code tool in favor of specialized tools 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Fahad
15
tools/__init__.py
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15
tools/__init__.py
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@@ -0,0 +1,15 @@
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"""
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Tool implementations for Gemini MCP Server
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"""
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from .think_deeper import ThinkDeeperTool
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from .review_code import ReviewCodeTool
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from .debug_issue import DebugIssueTool
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from .analyze import AnalyzeTool
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__all__ = [
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"ThinkDeeperTool",
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"ReviewCodeTool",
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"DebugIssueTool",
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"AnalyzeTool",
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]
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151
tools/analyze.py
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151
tools/analyze.py
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"""
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Analyze tool - General-purpose code and file analysis
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"""
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from typing import Dict, Any, List, Optional
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from pydantic import Field
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from .base import BaseTool, ToolRequest
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from prompts import ANALYZE_PROMPT
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from utils import read_files, check_token_limit
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from config import TEMPERATURE_ANALYTICAL, MAX_CONTEXT_TOKENS
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class AnalyzeRequest(ToolRequest):
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"""Request model for analyze tool"""
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files: List[str] = Field(..., description="Files to analyze")
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question: str = Field(..., description="What to analyze or look for")
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analysis_type: Optional[str] = Field(
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None,
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description="Type of analysis: architecture|performance|security|quality|general",
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)
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output_format: Optional[str] = Field(
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"detailed", description="Output format: summary|detailed|actionable"
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)
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class AnalyzeTool(BaseTool):
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"""General-purpose file and code analysis tool"""
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def get_name(self) -> str:
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return "analyze"
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def get_description(self) -> str:
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return (
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"ANALYZE FILES & CODE - General-purpose analysis for understanding code. "
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"Use this for examining files, understanding architecture, or investigating specific aspects. "
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"Triggers: 'analyze these files', 'examine this code', 'understand this'. "
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"Perfect for: codebase exploration, dependency analysis, pattern detection. "
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"Always uses file paths for clean terminal output."
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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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"files": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Files to analyze",
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},
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"question": {
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"type": "string",
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"description": "What to analyze or look for",
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},
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"analysis_type": {
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"type": "string",
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"enum": [
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"architecture",
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"performance",
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"security",
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"quality",
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"general",
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],
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"description": "Type of analysis to perform",
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},
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"output_format": {
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"type": "string",
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"enum": ["summary", "detailed", "actionable"],
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"default": "detailed",
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"description": "How to format the output",
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},
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"temperature": {
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"type": "number",
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"description": "Temperature (0-1, default 0.2)",
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"minimum": 0,
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"maximum": 1,
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},
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},
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"required": ["files", "question"],
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}
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def get_system_prompt(self) -> str:
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return ANALYZE_PROMPT
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def get_default_temperature(self) -> float:
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return TEMPERATURE_ANALYTICAL
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def get_request_model(self):
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return AnalyzeRequest
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async def prepare_prompt(self, request: AnalyzeRequest) -> str:
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"""Prepare the analysis prompt"""
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# Read all files
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file_content, summary = read_files(request.files)
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# Check token limits
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within_limit, estimated_tokens = check_token_limit(file_content)
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if not within_limit:
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raise ValueError(
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f"Files too large (~{estimated_tokens:,} tokens). "
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f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens."
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)
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# Build analysis instructions
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analysis_focus = []
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if request.analysis_type:
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type_focus = {
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"architecture": "Focus on architectural patterns, structure, and design decisions",
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"performance": "Focus on performance characteristics and optimization opportunities",
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"security": "Focus on security implications and potential vulnerabilities",
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"quality": "Focus on code quality, maintainability, and best practices",
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"general": "Provide a comprehensive general analysis",
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}
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analysis_focus.append(type_focus.get(request.analysis_type, ""))
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if request.output_format == "summary":
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analysis_focus.append("Provide a concise summary of key findings")
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elif request.output_format == "actionable":
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analysis_focus.append(
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"Focus on actionable insights and specific recommendations"
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)
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focus_instruction = "\n".join(analysis_focus) if analysis_focus else ""
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# Combine everything
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full_prompt = f"""{self.get_system_prompt()}
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{focus_instruction}
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=== USER QUESTION ===
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{request.question}
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=== END QUESTION ===
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=== FILES TO ANALYZE ===
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{file_content}
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=== END FILES ===
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Please analyze these files to answer the user's question."""
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return full_prompt
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def format_response(self, response: str, request: AnalyzeRequest) -> str:
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"""Format the analysis response"""
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header = f"Analysis: {request.question[:50]}..."
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if request.analysis_type:
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header = f"{request.analysis_type.upper()} Analysis"
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summary_text = f"Analyzed {len(request.files)} file(s)"
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return f"{header}\n{summary_text}\n{'=' * 50}\n\n{response}"
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128
tools/base.py
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128
tools/base.py
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"""
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Base class for all Gemini MCP tools
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"""
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from abc import ABC, abstractmethod
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from typing import Dict, Any, List, Optional
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from pydantic import BaseModel, Field
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import google.generativeai as genai
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from mcp.types import TextContent
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class ToolRequest(BaseModel):
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"""Base request model for all tools"""
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model: Optional[str] = Field(
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None, description="Model to use (defaults to Gemini 2.5 Pro)"
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)
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max_tokens: Optional[int] = Field(
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8192, description="Maximum number of tokens in response"
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)
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temperature: Optional[float] = Field(
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None, description="Temperature for response (tool-specific defaults)"
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)
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class BaseTool(ABC):
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"""Base class for all Gemini tools"""
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def __init__(self):
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self.name = self.get_name()
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self.description = self.get_description()
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self.default_temperature = self.get_default_temperature()
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@abstractmethod
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def get_name(self) -> str:
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"""Return the tool name"""
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pass
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@abstractmethod
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def get_description(self) -> str:
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"""Return the verbose tool description for Claude"""
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pass
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@abstractmethod
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def get_input_schema(self) -> Dict[str, Any]:
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"""Return the JSON schema for tool inputs"""
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pass
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@abstractmethod
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def get_system_prompt(self) -> str:
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"""Return the system prompt for this tool"""
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pass
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def get_default_temperature(self) -> float:
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"""Return default temperature for this tool"""
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return 0.5
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@abstractmethod
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def get_request_model(self):
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"""Return the Pydantic model for request validation"""
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pass
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async def execute(self, arguments: Dict[str, Any]) -> List[TextContent]:
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"""Execute the tool with given arguments"""
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try:
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# Validate request
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request_model = self.get_request_model()
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request = request_model(**arguments)
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# Prepare the prompt
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prompt = await self.prepare_prompt(request)
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# Get model configuration
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from config import DEFAULT_MODEL
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model_name = getattr(request, "model", None) or DEFAULT_MODEL
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temperature = getattr(request, "temperature", None)
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if temperature is None:
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temperature = self.get_default_temperature()
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max_tokens = getattr(request, "max_tokens", 8192)
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# Create and configure model
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model = self.create_model(model_name, temperature, max_tokens)
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# Generate response
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response = model.generate_content(prompt)
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# Handle response
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if response.candidates and response.candidates[0].content.parts:
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text = response.candidates[0].content.parts[0].text
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else:
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finish_reason = (
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response.candidates[0].finish_reason
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if response.candidates
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else "Unknown"
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)
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text = f"Response blocked or incomplete. Finish reason: {finish_reason}"
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# Format response
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formatted_response = self.format_response(text, request)
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return [TextContent(type="text", text=formatted_response)]
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except Exception as e:
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error_msg = f"Error in {self.name}: {str(e)}"
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return [TextContent(type="text", text=error_msg)]
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@abstractmethod
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async def prepare_prompt(self, request) -> str:
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"""Prepare the full prompt for Gemini"""
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pass
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def format_response(self, response: str, request) -> str:
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"""Format the response for display (can be overridden)"""
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return response
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def create_model(
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self, model_name: str, temperature: float, max_tokens: int
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) -> genai.GenerativeModel:
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"""Create a configured Gemini model"""
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return genai.GenerativeModel(
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model_name=model_name,
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generation_config={
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"temperature": temperature,
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"max_output_tokens": max_tokens,
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"candidate_count": 1,
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},
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)
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145
tools/debug_issue.py
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145
tools/debug_issue.py
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"""
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Debug Issue tool - Root cause analysis and debugging assistance
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"""
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from typing import Dict, Any, List, Optional
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from pydantic import Field
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from .base import BaseTool, ToolRequest
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from prompts import DEBUG_ISSUE_PROMPT
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from utils import read_files, check_token_limit
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from config import TEMPERATURE_ANALYTICAL, MAX_CONTEXT_TOKENS
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class DebugIssueRequest(ToolRequest):
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"""Request model for debug_issue tool"""
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error_description: str = Field(
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..., description="Error message, symptoms, or issue description"
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)
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error_context: Optional[str] = Field(
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None, description="Stack trace, logs, or additional error context"
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)
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relevant_files: Optional[List[str]] = Field(
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None, description="Files that might be related to the issue"
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)
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runtime_info: Optional[str] = Field(
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None, description="Environment, versions, or runtime information"
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)
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previous_attempts: Optional[str] = Field(
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None, description="What has been tried already"
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)
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class DebugIssueTool(BaseTool):
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"""Advanced debugging and root cause analysis tool"""
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def get_name(self) -> str:
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return "debug_issue"
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def get_description(self) -> str:
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return (
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"DEBUG & ROOT CAUSE ANALYSIS - Expert debugging for complex issues. "
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"Use this when you need help tracking down bugs or understanding errors. "
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"Triggers: 'debug this', 'why is this failing', 'root cause', 'trace error'. "
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"I'll analyze the issue, find root causes, and provide step-by-step solutions. "
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"Include error messages, stack traces, and relevant code for best results."
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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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"error_description": {
|
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"type": "string",
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"description": "Error message, symptoms, or issue description",
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},
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"error_context": {
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"type": "string",
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"description": "Stack trace, logs, or additional error context",
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},
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"relevant_files": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Files that might be related to the issue",
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},
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"runtime_info": {
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"type": "string",
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"description": "Environment, versions, or runtime information",
|
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},
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"previous_attempts": {
|
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"type": "string",
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"description": "What has been tried already",
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},
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"temperature": {
|
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"type": "number",
|
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"description": "Temperature (0-1, default 0.2 for accuracy)",
|
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"minimum": 0,
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"maximum": 1,
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},
|
||||
},
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"required": ["error_description"],
|
||||
}
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def get_system_prompt(self) -> str:
|
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return DEBUG_ISSUE_PROMPT
|
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|
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def get_default_temperature(self) -> float:
|
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return TEMPERATURE_ANALYTICAL
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|
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def get_request_model(self):
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return DebugIssueRequest
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async def prepare_prompt(self, request: DebugIssueRequest) -> str:
|
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"""Prepare the debugging prompt"""
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# Build context sections
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context_parts = [
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||||
f"=== ISSUE DESCRIPTION ===\n{request.error_description}\n=== END DESCRIPTION ==="
|
||||
]
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||||
|
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if request.error_context:
|
||||
context_parts.append(
|
||||
f"\n=== ERROR CONTEXT/STACK TRACE ===\n{request.error_context}\n=== END CONTEXT ==="
|
||||
)
|
||||
|
||||
if request.runtime_info:
|
||||
context_parts.append(
|
||||
f"\n=== RUNTIME INFORMATION ===\n{request.runtime_info}\n=== END RUNTIME ==="
|
||||
)
|
||||
|
||||
if request.previous_attempts:
|
||||
context_parts.append(
|
||||
f"\n=== PREVIOUS ATTEMPTS ===\n{request.previous_attempts}\n=== END ATTEMPTS ==="
|
||||
)
|
||||
|
||||
# Add relevant files if provided
|
||||
if request.relevant_files:
|
||||
file_content, _ = read_files(request.relevant_files)
|
||||
context_parts.append(
|
||||
f"\n=== RELEVANT CODE ===\n{file_content}\n=== END CODE ==="
|
||||
)
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||||
|
||||
full_context = "\n".join(context_parts)
|
||||
|
||||
# Check token limits
|
||||
within_limit, estimated_tokens = check_token_limit(full_context)
|
||||
if not within_limit:
|
||||
raise ValueError(
|
||||
f"Context too large (~{estimated_tokens:,} tokens). "
|
||||
f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens."
|
||||
)
|
||||
|
||||
# Combine everything
|
||||
full_prompt = f"""{self.get_system_prompt()}
|
||||
|
||||
{full_context}
|
||||
|
||||
Please debug this issue following the structured format in the system prompt.
|
||||
Focus on finding the root cause and providing actionable solutions."""
|
||||
|
||||
return full_prompt
|
||||
|
||||
def format_response(
|
||||
self, response: str, request: DebugIssueRequest
|
||||
) -> str:
|
||||
"""Format the debugging response"""
|
||||
return f"Debug Analysis\n{'=' * 50}\n\n{response}"
|
||||
160
tools/review_code.py
Normal file
160
tools/review_code.py
Normal file
@@ -0,0 +1,160 @@
|
||||
"""
|
||||
Code Review tool - Comprehensive code analysis and review
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List, Optional
|
||||
from pydantic import Field
|
||||
from .base import BaseTool, ToolRequest
|
||||
from prompts import REVIEW_CODE_PROMPT
|
||||
from utils import read_files, check_token_limit
|
||||
from config import TEMPERATURE_ANALYTICAL, MAX_CONTEXT_TOKENS
|
||||
|
||||
|
||||
class ReviewCodeRequest(ToolRequest):
|
||||
"""Request model for review_code tool"""
|
||||
|
||||
files: List[str] = Field(..., description="Code files to review")
|
||||
review_type: str = Field(
|
||||
"full", description="Type of review: full|security|performance|quick"
|
||||
)
|
||||
focus_on: Optional[str] = Field(
|
||||
None, description="Specific aspects to focus on during review"
|
||||
)
|
||||
standards: Optional[str] = Field(
|
||||
None, description="Coding standards or guidelines to enforce"
|
||||
)
|
||||
severity_filter: str = Field(
|
||||
"all",
|
||||
description="Minimum severity to report: critical|high|medium|all",
|
||||
)
|
||||
|
||||
|
||||
class ReviewCodeTool(BaseTool):
|
||||
"""Professional code review tool"""
|
||||
|
||||
def get_name(self) -> str:
|
||||
return "review_code"
|
||||
|
||||
def get_description(self) -> str:
|
||||
return (
|
||||
"PROFESSIONAL CODE REVIEW - Comprehensive analysis for bugs, security, and quality. "
|
||||
"Use this for thorough code review with actionable feedback. "
|
||||
"Triggers: 'review this code', 'check for issues', 'find bugs', 'security audit'. "
|
||||
"I'll identify issues by severity (Critical→High→Medium→Low) with specific fixes. "
|
||||
"Supports focused reviews: security, performance, or quick checks."
|
||||
)
|
||||
|
||||
def get_input_schema(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"files": {
|
||||
"type": "array",
|
||||
"items": {"type": "string"},
|
||||
"description": "Code files to review",
|
||||
},
|
||||
"review_type": {
|
||||
"type": "string",
|
||||
"enum": ["full", "security", "performance", "quick"],
|
||||
"default": "full",
|
||||
"description": "Type of review to perform",
|
||||
},
|
||||
"focus_on": {
|
||||
"type": "string",
|
||||
"description": "Specific aspects to focus on",
|
||||
},
|
||||
"standards": {
|
||||
"type": "string",
|
||||
"description": "Coding standards to enforce",
|
||||
},
|
||||
"severity_filter": {
|
||||
"type": "string",
|
||||
"enum": ["critical", "high", "medium", "all"],
|
||||
"default": "all",
|
||||
"description": "Minimum severity level to report",
|
||||
},
|
||||
"temperature": {
|
||||
"type": "number",
|
||||
"description": "Temperature (0-1, default 0.2 for consistency)",
|
||||
"minimum": 0,
|
||||
"maximum": 1,
|
||||
},
|
||||
},
|
||||
"required": ["files"],
|
||||
}
|
||||
|
||||
def get_system_prompt(self) -> str:
|
||||
return REVIEW_CODE_PROMPT
|
||||
|
||||
def get_default_temperature(self) -> float:
|
||||
return TEMPERATURE_ANALYTICAL
|
||||
|
||||
def get_request_model(self):
|
||||
return ReviewCodeRequest
|
||||
|
||||
async def prepare_prompt(self, request: ReviewCodeRequest) -> str:
|
||||
"""Prepare the code review prompt"""
|
||||
# Read all files
|
||||
file_content, summary = read_files(request.files)
|
||||
|
||||
# Check token limits
|
||||
within_limit, estimated_tokens = check_token_limit(file_content)
|
||||
if not within_limit:
|
||||
raise ValueError(
|
||||
f"Code too large (~{estimated_tokens:,} tokens). "
|
||||
f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens."
|
||||
)
|
||||
|
||||
# Build review instructions
|
||||
review_focus = []
|
||||
if request.review_type == "security":
|
||||
review_focus.append(
|
||||
"Focus on security vulnerabilities and authentication issues"
|
||||
)
|
||||
elif request.review_type == "performance":
|
||||
review_focus.append(
|
||||
"Focus on performance bottlenecks and optimization opportunities"
|
||||
)
|
||||
elif request.review_type == "quick":
|
||||
review_focus.append(
|
||||
"Provide a quick review focusing on critical issues only"
|
||||
)
|
||||
|
||||
if request.focus_on:
|
||||
review_focus.append(
|
||||
f"Pay special attention to: {request.focus_on}"
|
||||
)
|
||||
|
||||
if request.standards:
|
||||
review_focus.append(
|
||||
f"Enforce these standards: {request.standards}"
|
||||
)
|
||||
|
||||
if request.severity_filter != "all":
|
||||
review_focus.append(
|
||||
f"Only report issues of {request.severity_filter} severity or higher"
|
||||
)
|
||||
|
||||
focus_instruction = "\n".join(review_focus) if review_focus else ""
|
||||
|
||||
# Combine everything
|
||||
full_prompt = f"""{self.get_system_prompt()}
|
||||
|
||||
{focus_instruction}
|
||||
|
||||
=== CODE TO REVIEW ===
|
||||
{file_content}
|
||||
=== END CODE ===
|
||||
|
||||
Please provide a comprehensive code review following the format specified in the system prompt."""
|
||||
|
||||
return full_prompt
|
||||
|
||||
def format_response(
|
||||
self, response: str, request: ReviewCodeRequest
|
||||
) -> str:
|
||||
"""Format the review response"""
|
||||
header = f"Code Review ({request.review_type.upper()})"
|
||||
if request.focus_on:
|
||||
header += f" - Focus: {request.focus_on}"
|
||||
return f"{header}\n{'=' * 50}\n\n{response}"
|
||||
145
tools/think_deeper.py
Normal file
145
tools/think_deeper.py
Normal file
@@ -0,0 +1,145 @@
|
||||
"""
|
||||
Think Deeper tool - Extended reasoning and problem-solving
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List, Optional
|
||||
from pydantic import Field
|
||||
from .base import BaseTool, ToolRequest
|
||||
from prompts import THINK_DEEPER_PROMPT
|
||||
from utils import read_files, check_token_limit
|
||||
from config import TEMPERATURE_CREATIVE, MAX_CONTEXT_TOKENS
|
||||
|
||||
|
||||
class ThinkDeeperRequest(ToolRequest):
|
||||
"""Request model for think_deeper tool"""
|
||||
|
||||
current_analysis: str = Field(
|
||||
..., description="Claude's current thinking/analysis to extend"
|
||||
)
|
||||
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.)",
|
||||
)
|
||||
reference_files: Optional[List[str]] = Field(
|
||||
None, description="Optional file paths for additional context"
|
||||
)
|
||||
|
||||
|
||||
class ThinkDeeperTool(BaseTool):
|
||||
"""Extended thinking and reasoning tool"""
|
||||
|
||||
def get_name(self) -> str:
|
||||
return "think_deeper"
|
||||
|
||||
def get_description(self) -> str:
|
||||
return (
|
||||
"EXTENDED THINKING & REASONING - Your deep thinking partner for complex problems. "
|
||||
"Use this when you need to extend your analysis, explore alternatives, or validate approaches. "
|
||||
"Perfect for: architecture decisions, complex bugs, performance challenges, security analysis. "
|
||||
"Triggers: 'think deeper', 'ultrathink', 'extend my analysis', 'explore alternatives'. "
|
||||
"I'll challenge assumptions, find edge cases, and provide alternative solutions."
|
||||
)
|
||||
|
||||
def get_input_schema(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"current_analysis": {
|
||||
"type": "string",
|
||||
"description": "Your current thinking/analysis to extend and validate",
|
||||
},
|
||||
"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.)",
|
||||
},
|
||||
"reference_files": {
|
||||
"type": "array",
|
||||
"items": {"type": "string"},
|
||||
"description": "Optional file paths for additional context",
|
||||
},
|
||||
"temperature": {
|
||||
"type": "number",
|
||||
"description": "Temperature for creative thinking (0-1, default 0.7)",
|
||||
"minimum": 0,
|
||||
"maximum": 1,
|
||||
},
|
||||
"max_tokens": {
|
||||
"type": "integer",
|
||||
"description": "Maximum tokens in response",
|
||||
"default": 8192,
|
||||
},
|
||||
},
|
||||
"required": ["current_analysis"],
|
||||
}
|
||||
|
||||
def get_system_prompt(self) -> str:
|
||||
return THINK_DEEPER_PROMPT
|
||||
|
||||
def get_default_temperature(self) -> float:
|
||||
return TEMPERATURE_CREATIVE
|
||||
|
||||
def get_request_model(self):
|
||||
return ThinkDeeperRequest
|
||||
|
||||
async def prepare_prompt(self, request: ThinkDeeperRequest) -> str:
|
||||
"""Prepare the full prompt for extended thinking"""
|
||||
# Build context parts
|
||||
context_parts = [
|
||||
f"=== CLAUDE'S CURRENT ANALYSIS ===\n{request.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.reference_files:
|
||||
file_content, _ = read_files(request.reference_files)
|
||||
context_parts.append(
|
||||
f"\n=== REFERENCE FILES ===\n{file_content}\n=== END FILES ==="
|
||||
)
|
||||
|
||||
full_context = "\n".join(context_parts)
|
||||
|
||||
# Check token limits
|
||||
within_limit, estimated_tokens = check_token_limit(full_context)
|
||||
if not within_limit:
|
||||
raise ValueError(
|
||||
f"Context too large (~{estimated_tokens:,} tokens). "
|
||||
f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens."
|
||||
)
|
||||
|
||||
# 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."
|
||||
|
||||
# Combine system prompt with context
|
||||
full_prompt = f"""{self.get_system_prompt()}{focus_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: ThinkDeeperRequest
|
||||
) -> str:
|
||||
"""Format the response with clear attribution"""
|
||||
return f"Extended Analysis by Gemini:\n\n{response}"
|
||||
Reference in New Issue
Block a user