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
855
gemini_server.py
855
gemini_server.py
@@ -1,858 +1,11 @@
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#!/usr/bin/env python3
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"""
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Gemini MCP Server - Model Context Protocol server for Google Gemini
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Enhanced for large-scale code analysis with 1M token context window
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Gemini MCP Server - Entry point for backward compatibility
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This file exists to maintain compatibility with existing configurations.
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The main implementation is now in server.py
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"""
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from server import main
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import asyncio
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import json
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import os
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import sys
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import google.generativeai as genai
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from mcp.server import Server
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from mcp.server.models import InitializationOptions
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from mcp.server.stdio import stdio_server
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from mcp.types import TextContent, Tool
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from pydantic import BaseModel, Field
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# Version and metadata
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__version__ = "2.3.0"
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__updated__ = "2025-06-08"
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__author__ = "Fahad Gilani"
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# Default to Gemini 2.5 Pro Preview with maximum context
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DEFAULT_MODEL = "gemini-2.5-pro-preview-06-05"
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MAX_CONTEXT_TOKENS = 1000000 # 1M tokens
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# Developer-focused system prompt for Claude Code usage
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DEVELOPER_SYSTEM_PROMPT = """You are an expert software developer assistant working alongside Claude Code. \
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Your role is to extend Claude's capabilities when handling large codebases or complex analysis tasks.
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Core competencies:
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- Deep understanding of software architecture and design patterns
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- Expert-level debugging and root cause analysis
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- Performance optimization and scalability considerations
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- Security best practices and vulnerability identification
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- Clean code principles and refactoring strategies
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- Comprehensive testing approaches (unit, integration, e2e)
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- Modern development practices (CI/CD, DevOps, cloud-native)
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- Cross-platform and cross-language expertise
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Your approach:
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- Be precise and technical, avoiding unnecessary explanations
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- Provide actionable, concrete solutions with code examples
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- Consider edge cases and potential issues proactively
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- Focus on maintainability, readability, and long-term sustainability
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- Suggest modern, idiomatic solutions for the given language/framework
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- When reviewing code, prioritize critical issues first
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- Always validate your suggestions against best practices
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Remember: You're augmenting Claude Code's capabilities, especially for tasks requiring \
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extensive context or deep analysis that might exceed Claude's token limits."""
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# Extended thinking system prompt for collaborative analysis
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EXTENDED_THINKING_PROMPT = """You are a senior development partner collaborating with Claude Code on complex problems. \
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Claude has shared their analysis with you for deeper exploration and validation.
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Your role is to:
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1. Build upon Claude's thinking - identify gaps, extend ideas, and suggest alternatives
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2. Challenge assumptions constructively and identify potential issues
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3. Provide concrete, actionable insights that complement Claude's analysis
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4. Focus on aspects Claude might have missed or couldn't fully explore
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5. Suggest implementation strategies and architectural improvements
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Key areas to consider:
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- Edge cases and failure modes Claude might have overlooked
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- Performance implications at scale
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- Security vulnerabilities or attack vectors
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- Maintainability and technical debt considerations
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- Alternative approaches or design patterns
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- Integration challenges with existing systems
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- Testing strategies for complex scenarios
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Be direct and technical. Assume Claude and the user are experienced developers who want \
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deep, nuanced analysis rather than basic explanations."""
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class GeminiChatRequest(BaseModel):
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"""Request model for Gemini chat"""
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prompt: str = Field(..., description="The prompt to send to Gemini")
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system_prompt: Optional[str] = Field(
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None, description="Optional system prompt for context"
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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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0.5,
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description="Temperature for response randomness (0-1, default 0.5 for balanced accuracy/creativity)",
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)
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model: Optional[str] = Field(
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DEFAULT_MODEL, description=f"Model to use (defaults to {DEFAULT_MODEL})"
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)
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class CodeAnalysisRequest(BaseModel):
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"""Request model for code analysis"""
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files: Optional[List[str]] = Field(
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None, description="List of file paths to analyze"
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)
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code: Optional[str] = Field(None, description="Direct code content to analyze")
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question: str = Field(
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..., description="Question or analysis request about the code"
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)
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system_prompt: Optional[str] = Field(
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None, description="Optional system prompt for context"
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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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0.2,
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description="Temperature for code analysis (0-1, default 0.2 for high accuracy)",
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)
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model: Optional[str] = Field(
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DEFAULT_MODEL, description=f"Model to use (defaults to {DEFAULT_MODEL})"
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)
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verbose_output: Optional[bool] = Field(
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False, description="Show file contents in terminal output"
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)
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class FileAnalysisRequest(BaseModel):
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"""Request model for file analysis"""
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files: List[str] = Field(..., description="List of file paths to analyze")
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question: str = Field(
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..., description="Question or analysis request about the files"
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)
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system_prompt: Optional[str] = Field(
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None, description="Optional system prompt for context"
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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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0.2,
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description="Temperature for analysis (0-1, default 0.2 for high accuracy)",
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)
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model: Optional[str] = Field(
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DEFAULT_MODEL, description=f"Model to use (defaults to {DEFAULT_MODEL})"
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)
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class ExtendedThinkRequest(BaseModel):
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"""Request model for extended thinking with Gemini"""
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thought_process: str = Field(
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..., description="Claude's analysis, thoughts, plans, or outlines to extend"
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)
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context: Optional[str] = Field(
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None, description="Additional context about the problem or goal"
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)
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files: Optional[List[str]] = Field(
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None, description="Optional file paths for additional context"
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)
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focus: Optional[str] = Field(
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None,
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description="Specific focus area: architecture, bugs, performance, security, etc.",
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)
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system_prompt: Optional[str] = Field(
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None, description="Optional system prompt for context"
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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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0.7,
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description="Temperature for creative thinking (0-1, default 0.7 for balanced creativity)",
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)
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model: Optional[str] = Field(
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DEFAULT_MODEL, description=f"Model to use (defaults to {DEFAULT_MODEL})"
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)
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# Create the MCP server instance
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server: Server = Server("gemini-server")
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# Configure Gemini API
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def configure_gemini():
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"""Configure the Gemini API with API key from environment"""
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api_key = os.getenv("GEMINI_API_KEY")
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if not api_key:
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raise ValueError("GEMINI_API_KEY environment variable is not set")
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genai.configure(api_key=api_key)
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def read_file_content(file_path: str) -> str:
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"""Read content from a file with error handling - for backward compatibility"""
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return read_file_content_for_gemini(file_path)
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def read_file_content_for_gemini(file_path: str) -> str:
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"""Read content from a file with proper formatting for Gemini"""
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try:
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path = Path(file_path)
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if not path.exists():
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return f"\n--- FILE NOT FOUND: {file_path} ---\nError: File does not exist\n--- END FILE ---\n"
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if not path.is_file():
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return f"\n--- NOT A FILE: {file_path} ---\nError: Path is not a file\n--- END FILE ---\n"
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# Read the file
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with open(path, "r", encoding="utf-8") as f:
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content = f.read()
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# Format with clear delimiters for Gemini
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return f"\n--- BEGIN FILE: {file_path} ---\n{content}\n--- END FILE: {file_path} ---\n"
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except Exception as e:
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return f"\n--- ERROR READING FILE: {file_path} ---\nError: {str(e)}\n--- END FILE ---\n"
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def prepare_code_context(
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files: Optional[List[str]], code: Optional[str]
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) -> Tuple[str, str]:
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"""Prepare code context from files and/or direct code
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Returns: (context_for_gemini, summary_for_terminal)
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"""
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context_parts = []
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summary_parts = []
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# Add file contents
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if files:
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summary_parts.append(f"Analyzing {len(files)} file(s):")
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for file_path in files:
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# Get file content for Gemini
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file_content = read_file_content_for_gemini(file_path)
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context_parts.append(file_content)
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# Create summary with small excerpt for terminal
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path = Path(file_path)
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if path.exists() and path.is_file():
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size = path.stat().st_size
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try:
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with open(path, "r", encoding="utf-8") as f:
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# Read first few lines for preview
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preview_lines = []
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for i, line in enumerate(f):
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if i >= 3: # Show max 3 lines
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break
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preview_lines.append(line.rstrip())
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preview = "\n".join(preview_lines)
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if len(preview) > 100:
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preview = preview[:100] + "..."
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summary_parts.append(f" {file_path} ({size:,} bytes)")
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if preview.strip():
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summary_parts.append(f" Preview: {preview[:50]}...")
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except Exception:
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summary_parts.append(f" {file_path} ({size:,} bytes)")
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else:
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summary_parts.append(f" {file_path} (not found)")
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# Add direct code
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if code:
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formatted_code = (
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f"\n--- BEGIN DIRECT CODE ---\n{code}\n--- END DIRECT CODE ---\n"
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)
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context_parts.append(formatted_code)
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preview = code[:100] + "..." if len(code) > 100 else code
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summary_parts.append(f"Direct code provided ({len(code):,} characters)")
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summary_parts.append(f" Preview: {preview}")
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full_context = "\n\n".join(context_parts)
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summary = "\n".join(summary_parts)
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return full_context, summary
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@server.list_tools()
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async def handle_list_tools() -> List[Tool]:
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"""List all available tools"""
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return [
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Tool(
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name="chat",
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description="Chat with Gemini (optimized for 2.5 Pro with 1M context)",
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inputSchema={
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"type": "object",
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"properties": {
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"prompt": {
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"type": "string",
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"description": "The prompt to send to Gemini",
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},
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"system_prompt": {
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"type": "string",
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"description": "Optional system prompt for context",
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},
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"max_tokens": {
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"type": "integer",
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"description": "Maximum number of tokens in response",
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"default": 8192,
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},
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"temperature": {
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"type": "number",
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"description": "Temperature for response randomness (0-1, default 0.5 for "
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"balanced accuracy/creativity)",
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"default": 0.5,
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"minimum": 0,
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"maximum": 1,
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},
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"model": {
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"type": "string",
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"description": f"Model to use (defaults to {DEFAULT_MODEL})",
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"default": DEFAULT_MODEL,
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},
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},
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"required": ["prompt"],
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},
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),
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Tool(
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name="analyze_code",
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description="Analyze code files or snippets with Gemini's 1M context window. "
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"For large content, use file paths to avoid terminal clutter.",
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inputSchema={
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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": "List of file paths to analyze",
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},
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"code": {
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"type": "string",
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"description": "Direct code content to analyze "
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"(use for small snippets only; prefer files for large content)",
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},
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"question": {
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"type": "string",
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"description": "Question or analysis request about the code",
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},
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"system_prompt": {
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"type": "string",
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"description": "Optional system prompt for context",
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},
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"max_tokens": {
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"type": "integer",
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"description": "Maximum number of tokens in response",
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"default": 8192,
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},
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"temperature": {
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"type": "number",
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"description": "Temperature for code analysis (0-1, default 0.2 for high accuracy)",
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"default": 0.2,
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"minimum": 0,
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"maximum": 1,
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},
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"model": {
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"type": "string",
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"description": f"Model to use (defaults to {DEFAULT_MODEL})",
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"default": DEFAULT_MODEL,
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},
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},
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"required": ["question"],
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},
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),
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Tool(
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name="list_models",
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description="List available Gemini models",
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inputSchema={"type": "object", "properties": {}},
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),
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Tool(
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name="get_version",
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description="Get the version and metadata of the Gemini MCP Server",
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inputSchema={"type": "object", "properties": {}},
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),
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Tool(
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name="analyze_file",
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description="Analyze files with Gemini - always uses file paths for clean terminal output",
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inputSchema={
|
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"type": "object",
|
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"properties": {
|
||||
"files": {
|
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"type": "array",
|
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"items": {"type": "string"},
|
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"description": "List of file paths to analyze",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "Question or analysis request about the files",
|
||||
},
|
||||
"system_prompt": {
|
||||
"type": "string",
|
||||
"description": "Optional system prompt for context",
|
||||
},
|
||||
"max_tokens": {
|
||||
"type": "integer",
|
||||
"description": "Maximum number of tokens in response",
|
||||
"default": 8192,
|
||||
},
|
||||
"temperature": {
|
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"type": "number",
|
||||
"description": "Temperature for analysis (0-1, default 0.2 for high accuracy)",
|
||||
"default": 0.2,
|
||||
"minimum": 0,
|
||||
"maximum": 1,
|
||||
},
|
||||
"model": {
|
||||
"type": "string",
|
||||
"description": f"Model to use (defaults to {DEFAULT_MODEL})",
|
||||
"default": DEFAULT_MODEL,
|
||||
},
|
||||
},
|
||||
"required": ["files", "question"],
|
||||
},
|
||||
),
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Tool(
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||||
name="extended_think",
|
||||
description="Collaborate with Gemini on complex problems - share Claude's analysis for deeper insights",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"thought_process": {
|
||||
"type": "string",
|
||||
"description": "Claude's analysis, thoughts, plans, or outlines to extend",
|
||||
},
|
||||
"context": {
|
||||
"type": "string",
|
||||
"description": "Additional context about the problem or goal",
|
||||
},
|
||||
"files": {
|
||||
"type": "array",
|
||||
"items": {"type": "string"},
|
||||
"description": "Optional file paths for additional context",
|
||||
},
|
||||
"focus": {
|
||||
"type": "string",
|
||||
"description": "Specific focus area: architecture, bugs, performance, security, etc.",
|
||||
},
|
||||
"system_prompt": {
|
||||
"type": "string",
|
||||
"description": "Optional system prompt for context",
|
||||
},
|
||||
"max_tokens": {
|
||||
"type": "integer",
|
||||
"description": "Maximum number of tokens in response",
|
||||
"default": 8192,
|
||||
},
|
||||
"temperature": {
|
||||
"type": "number",
|
||||
"description": "Temperature for creative thinking (0-1, default 0.7)",
|
||||
"default": 0.7,
|
||||
"minimum": 0,
|
||||
"maximum": 1,
|
||||
},
|
||||
"model": {
|
||||
"type": "string",
|
||||
"description": f"Model to use (defaults to {DEFAULT_MODEL})",
|
||||
"default": DEFAULT_MODEL,
|
||||
},
|
||||
},
|
||||
"required": ["thought_process"],
|
||||
},
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
@server.call_tool()
|
||||
async def handle_call_tool(name: str, arguments: Dict[str, Any]) -> List[TextContent]:
|
||||
"""Handle tool execution requests"""
|
||||
|
||||
if name == "chat":
|
||||
# Validate request
|
||||
request = GeminiChatRequest(**arguments)
|
||||
|
||||
try:
|
||||
# Use the specified model with optimized settings
|
||||
model_name = request.model or DEFAULT_MODEL
|
||||
temperature = (
|
||||
request.temperature if request.temperature is not None else 0.5
|
||||
)
|
||||
max_tokens = request.max_tokens if request.max_tokens is not None else 8192
|
||||
|
||||
model = genai.GenerativeModel(
|
||||
model_name=model_name,
|
||||
generation_config={
|
||||
"temperature": temperature,
|
||||
"max_output_tokens": max_tokens,
|
||||
"candidate_count": 1,
|
||||
},
|
||||
)
|
||||
|
||||
# Prepare the prompt with automatic developer context if no system prompt provided
|
||||
if request.system_prompt:
|
||||
full_prompt = f"{request.system_prompt}\n\n{request.prompt}"
|
||||
else:
|
||||
# Auto-inject developer system prompt for better Claude Code integration
|
||||
full_prompt = f"{DEVELOPER_SYSTEM_PROMPT}\n\n{request.prompt}"
|
||||
|
||||
# Generate response
|
||||
response = model.generate_content(full_prompt)
|
||||
|
||||
# Handle response based on finish reason
|
||||
if response.candidates and response.candidates[0].content.parts:
|
||||
text = response.candidates[0].content.parts[0].text
|
||||
else:
|
||||
# Handle safety filters or other issues
|
||||
finish_reason = (
|
||||
response.candidates[0].finish_reason
|
||||
if response.candidates
|
||||
else "Unknown"
|
||||
)
|
||||
text = f"Response blocked or incomplete. Finish reason: {finish_reason}"
|
||||
|
||||
return [TextContent(type="text", text=text)]
|
||||
|
||||
except Exception as e:
|
||||
return [
|
||||
TextContent(type="text", text=f"Error calling Gemini API: {str(e)}")
|
||||
]
|
||||
|
||||
elif name == "analyze_code":
|
||||
# Validate request
|
||||
request_analysis = CodeAnalysisRequest(**arguments)
|
||||
|
||||
# Check that we have either files or code
|
||||
if not request_analysis.files and not request_analysis.code:
|
||||
return [
|
||||
TextContent(
|
||||
type="text",
|
||||
text="Error: Must provide either 'files' or 'code' parameter",
|
||||
)
|
||||
]
|
||||
|
||||
try:
|
||||
# Prepare code context - always use non-verbose mode for Claude Code compatibility
|
||||
code_context, summary = prepare_code_context(
|
||||
request_analysis.files, request_analysis.code
|
||||
)
|
||||
|
||||
# Count approximate tokens (rough estimate: 1 token ≈ 4 characters)
|
||||
estimated_tokens = len(code_context) // 4
|
||||
if estimated_tokens > MAX_CONTEXT_TOKENS:
|
||||
return [
|
||||
TextContent(
|
||||
type="text",
|
||||
text=f"Error: Code context too large (~{estimated_tokens:,} tokens). "
|
||||
f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens.",
|
||||
)
|
||||
]
|
||||
|
||||
# Use the specified model with optimized settings for code analysis
|
||||
model_name = request_analysis.model or DEFAULT_MODEL
|
||||
temperature = (
|
||||
request_analysis.temperature
|
||||
if request_analysis.temperature is not None
|
||||
else 0.2
|
||||
)
|
||||
max_tokens = (
|
||||
request_analysis.max_tokens
|
||||
if request_analysis.max_tokens is not None
|
||||
else 8192
|
||||
)
|
||||
|
||||
model = genai.GenerativeModel(
|
||||
model_name=model_name,
|
||||
generation_config={
|
||||
"temperature": temperature,
|
||||
"max_output_tokens": max_tokens,
|
||||
"candidate_count": 1,
|
||||
},
|
||||
)
|
||||
|
||||
# Prepare the full prompt with enhanced developer context and clear structure
|
||||
system_prompt = request_analysis.system_prompt or DEVELOPER_SYSTEM_PROMPT
|
||||
full_prompt = f"""{system_prompt}
|
||||
|
||||
=== USER REQUEST ===
|
||||
{request_analysis.question}
|
||||
=== END USER REQUEST ===
|
||||
|
||||
=== CODE TO ANALYZE ===
|
||||
{code_context}
|
||||
=== END CODE TO ANALYZE ===
|
||||
|
||||
Please analyze the code above and respond to the user's request. The code files are clearly \
|
||||
marked with their paths and content boundaries."""
|
||||
|
||||
# Generate response
|
||||
response = model.generate_content(full_prompt)
|
||||
|
||||
# Handle response
|
||||
if response.candidates and response.candidates[0].content.parts:
|
||||
text = response.candidates[0].content.parts[0].text
|
||||
else:
|
||||
finish_reason = (
|
||||
response.candidates[0].finish_reason
|
||||
if response.candidates
|
||||
else "Unknown"
|
||||
)
|
||||
text = f"Response blocked or incomplete. Finish reason: {finish_reason}"
|
||||
|
||||
# Create a brief summary for terminal display
|
||||
if request_analysis.files or request_analysis.code:
|
||||
# Create a very brief summary for terminal
|
||||
brief_summary_parts = []
|
||||
if request_analysis.files:
|
||||
brief_summary_parts.append(
|
||||
f"Analyzing {len(request_analysis.files)} file(s)"
|
||||
)
|
||||
if request_analysis.code:
|
||||
code_preview = (
|
||||
request_analysis.code[:20] + "..."
|
||||
if len(request_analysis.code) > 20
|
||||
else request_analysis.code
|
||||
)
|
||||
brief_summary_parts.append(f"Direct code: {code_preview}")
|
||||
|
||||
brief_summary = " | ".join(brief_summary_parts)
|
||||
response_text = f"{brief_summary}\n\nGemini's Analysis:\n{text}"
|
||||
else:
|
||||
response_text = text
|
||||
|
||||
return [TextContent(type="text", text=response_text)]
|
||||
|
||||
except Exception as e:
|
||||
return [TextContent(type="text", text=f"Error analyzing code: {str(e)}")]
|
||||
|
||||
elif name == "list_models":
|
||||
try:
|
||||
# List available models
|
||||
models = []
|
||||
for model_info in genai.list_models():
|
||||
if (
|
||||
hasattr(model_info, "supported_generation_methods")
|
||||
and "generateContent" in model_info.supported_generation_methods
|
||||
):
|
||||
models.append(
|
||||
{
|
||||
"name": model_info.name,
|
||||
"display_name": getattr(
|
||||
model_info, "display_name", "Unknown"
|
||||
),
|
||||
"description": getattr(
|
||||
model_info, "description", "No description"
|
||||
),
|
||||
"is_default": model_info.name.endswith(DEFAULT_MODEL),
|
||||
}
|
||||
)
|
||||
|
||||
return [TextContent(type="text", text=json.dumps(models, indent=2))]
|
||||
|
||||
except Exception as e:
|
||||
return [TextContent(type="text", text=f"Error listing models: {str(e)}")]
|
||||
|
||||
elif name == "get_version":
|
||||
# Return version and metadata information
|
||||
version_info = {
|
||||
"version": __version__,
|
||||
"updated": __updated__,
|
||||
"author": __author__,
|
||||
"default_model": DEFAULT_MODEL,
|
||||
"max_context_tokens": f"{MAX_CONTEXT_TOKENS:,}",
|
||||
"python_version": f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}",
|
||||
"server_started": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
return [
|
||||
TextContent(
|
||||
type="text",
|
||||
text=f"""Gemini MCP Server v{__version__}
|
||||
Updated: {__updated__}
|
||||
Author: {__author__}
|
||||
|
||||
Configuration:
|
||||
- Default Model: {DEFAULT_MODEL}
|
||||
- Max Context: {MAX_CONTEXT_TOKENS:,} tokens
|
||||
- Python: {version_info['python_version']}
|
||||
- Started: {version_info['server_started']}
|
||||
|
||||
For updates, visit: https://github.com/BeehiveInnovations/gemini-mcp-server""",
|
||||
)
|
||||
]
|
||||
|
||||
elif name == "analyze_file":
|
||||
# Validate request
|
||||
request_file = FileAnalysisRequest(**arguments)
|
||||
|
||||
try:
|
||||
# Prepare code context from files
|
||||
code_context, summary = prepare_code_context(request_file.files, None)
|
||||
|
||||
# Count approximate tokens
|
||||
estimated_tokens = len(code_context) // 4
|
||||
if estimated_tokens > MAX_CONTEXT_TOKENS:
|
||||
return [
|
||||
TextContent(
|
||||
type="text",
|
||||
text=f"Error: File content too large (~{estimated_tokens:,} tokens). "
|
||||
f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens.",
|
||||
)
|
||||
]
|
||||
|
||||
# Use the specified model with optimized settings
|
||||
model_name = request_file.model or DEFAULT_MODEL
|
||||
temperature = (
|
||||
request_file.temperature if request_file.temperature is not None else 0.2
|
||||
)
|
||||
max_tokens = request_file.max_tokens if request_file.max_tokens is not None else 8192
|
||||
|
||||
model = genai.GenerativeModel(
|
||||
model_name=model_name,
|
||||
generation_config={
|
||||
"temperature": temperature,
|
||||
"max_output_tokens": max_tokens,
|
||||
"candidate_count": 1,
|
||||
},
|
||||
)
|
||||
|
||||
# Prepare prompt
|
||||
system_prompt = request_file.system_prompt or DEVELOPER_SYSTEM_PROMPT
|
||||
full_prompt = f"""{system_prompt}
|
||||
|
||||
=== USER REQUEST ===
|
||||
{request_file.question}
|
||||
=== END USER REQUEST ===
|
||||
|
||||
=== FILES TO ANALYZE ===
|
||||
{code_context}
|
||||
=== END FILES ===
|
||||
|
||||
Please analyze the files above and respond to the user's request."""
|
||||
|
||||
# Generate response
|
||||
response = model.generate_content(full_prompt)
|
||||
|
||||
# Handle response
|
||||
if response.candidates and response.candidates[0].content.parts:
|
||||
text = response.candidates[0].content.parts[0].text
|
||||
else:
|
||||
finish_reason = (
|
||||
response.candidates[0].finish_reason
|
||||
if response.candidates
|
||||
else "Unknown"
|
||||
)
|
||||
text = f"Response blocked or incomplete. Finish reason: {finish_reason}"
|
||||
|
||||
# Create a brief summary for terminal
|
||||
brief_summary = f"Analyzing {len(request_file.files)} file(s)"
|
||||
response_text = f"{brief_summary}\n\nGemini's Analysis:\n{text}"
|
||||
|
||||
return [TextContent(type="text", text=response_text)]
|
||||
|
||||
except Exception as e:
|
||||
return [TextContent(type="text", text=f"Error analyzing files: {str(e)}")]
|
||||
|
||||
elif name == "extended_think":
|
||||
# Validate request
|
||||
request_think = ExtendedThinkRequest(**arguments)
|
||||
|
||||
try:
|
||||
# Prepare context parts
|
||||
context_parts = [
|
||||
f"=== CLAUDE'S ANALYSIS ===\n{request_think.thought_process}\n=== END CLAUDE'S ANALYSIS ==="
|
||||
]
|
||||
|
||||
if request_think.context:
|
||||
context_parts.append(
|
||||
f"\n=== ADDITIONAL CONTEXT ===\n{request_think.context}\n=== END CONTEXT ==="
|
||||
)
|
||||
|
||||
# Add file contents if provided
|
||||
if request_think.files:
|
||||
file_context, _ = prepare_code_context(request_think.files, None)
|
||||
context_parts.append(
|
||||
f"\n=== REFERENCE FILES ===\n{file_context}\n=== END FILES ==="
|
||||
)
|
||||
|
||||
full_context = "\n".join(context_parts)
|
||||
|
||||
# Check token limits
|
||||
estimated_tokens = len(full_context) // 4
|
||||
if estimated_tokens > MAX_CONTEXT_TOKENS:
|
||||
return [
|
||||
TextContent(
|
||||
type="text",
|
||||
text=f"Error: Context too large (~{estimated_tokens:,} tokens). "
|
||||
f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens.",
|
||||
)
|
||||
]
|
||||
|
||||
# Use the specified model with creative settings
|
||||
model_name = request_think.model or DEFAULT_MODEL
|
||||
temperature = (
|
||||
request_think.temperature if request_think.temperature is not None else 0.7
|
||||
)
|
||||
max_tokens = request_think.max_tokens if request_think.max_tokens is not None else 8192
|
||||
|
||||
model = genai.GenerativeModel(
|
||||
model_name=model_name,
|
||||
generation_config={
|
||||
"temperature": temperature,
|
||||
"max_output_tokens": max_tokens,
|
||||
"candidate_count": 1,
|
||||
},
|
||||
)
|
||||
|
||||
# Prepare prompt with focus area if specified
|
||||
system_prompt = request_think.system_prompt or EXTENDED_THINKING_PROMPT
|
||||
focus_instruction = ""
|
||||
if request_think.focus:
|
||||
focus_instruction = f"\n\nFOCUS AREA: Please pay special attention to {request_think.focus} aspects."
|
||||
|
||||
full_prompt = f"""{system_prompt}{focus_instruction}
|
||||
|
||||
{full_context}
|
||||
|
||||
Build upon Claude's analysis with deeper insights, alternative approaches, and critical evaluation."""
|
||||
|
||||
# Generate response
|
||||
response = model.generate_content(full_prompt)
|
||||
|
||||
# Handle response
|
||||
if response.candidates and response.candidates[0].content.parts:
|
||||
text = response.candidates[0].content.parts[0].text
|
||||
else:
|
||||
finish_reason = (
|
||||
response.candidates[0].finish_reason
|
||||
if response.candidates
|
||||
else "Unknown"
|
||||
)
|
||||
text = f"Response blocked or incomplete. Finish reason: {finish_reason}"
|
||||
|
||||
# Create response with clear attribution
|
||||
response_text = f"Extended Analysis by Gemini:\n\n{text}"
|
||||
|
||||
return [TextContent(type="text", text=response_text)]
|
||||
|
||||
except Exception as e:
|
||||
return [
|
||||
TextContent(type="text", text=f"Error in extended thinking: {str(e)}")
|
||||
]
|
||||
|
||||
else:
|
||||
return [TextContent(type="text", text=f"Unknown tool: {name}")]
|
||||
|
||||
|
||||
async def main():
|
||||
"""Main entry point for the server"""
|
||||
# Configure Gemini API
|
||||
configure_gemini()
|
||||
|
||||
# Run the server using stdio transport
|
||||
async with stdio_server() as (read_stream, write_stream):
|
||||
await server.run(
|
||||
read_stream,
|
||||
write_stream,
|
||||
InitializationOptions(
|
||||
server_name="gemini", server_version="2.0.0", capabilities={"tools": {}}
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
||||
Reference in New Issue
Block a user