feat: add Claude-Gemini collaboration and chat capabilities
- Add collaboration demo showing dynamic context requests - Implement chat tool for general conversations and brainstorming - Add tool selection guide with clear boundaries - Introduce models configuration system - Update prompts for better tool descriptions - Refactor server to remove redundant functionality - Add comprehensive tests for collaboration features - Enhance base tool with collaborative features This enables Claude to request additional context from Gemini during tool execution, improving analysis quality and accuracy. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Fahad
@@ -3,6 +3,7 @@ Tool implementations for Gemini MCP Server
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"""
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from .analyze import AnalyzeTool
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from .chat import ChatTool
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from .debug_issue import DebugIssueTool
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from .review_code import ReviewCodeTool
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from .think_deeper import ThinkDeeperTool
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@@ -12,4 +13,5 @@ __all__ = [
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"ReviewCodeTool",
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"DebugIssueTool",
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"AnalyzeTool",
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"ChatTool",
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]
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@@ -5,12 +5,15 @@ Base class for all Gemini MCP tools
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional, Literal
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import os
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import json
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from google import genai
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from google.genai import types
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from mcp.types import TextContent
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from pydantic import BaseModel, Field
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from .models import ToolOutput, ClarificationRequest
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class ToolRequest(BaseModel):
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"""Base request model for all tools"""
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@@ -95,25 +98,83 @@ class BaseTool(ABC):
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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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# Handle response and create standardized output
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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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raw_text = response.candidates[0].content.parts[0].text
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# Check if this is a clarification request
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tool_output = self._parse_response(raw_text, request)
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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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tool_output = ToolOutput(
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status="error",
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content=f"Response blocked or incomplete. Finish reason: {finish_reason}",
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content_type="text",
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)
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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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# Serialize the standardized output as JSON
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return [TextContent(type="text", text=tool_output.model_dump_json())]
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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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error_output = ToolOutput(
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status="error",
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content=f"Error in {self.name}: {str(e)}",
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content_type="text",
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)
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return [TextContent(type="text", text=error_output.model_dump_json())]
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def _parse_response(self, raw_text: str, request) -> ToolOutput:
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"""Parse the raw response and determine if it's a clarification request"""
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try:
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# Try to parse as JSON to check for clarification requests
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potential_json = json.loads(raw_text.strip())
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if (
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isinstance(potential_json, dict)
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and potential_json.get("status") == "requires_clarification"
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):
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# Validate the clarification request structure
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clarification = ClarificationRequest(**potential_json)
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return ToolOutput(
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status="requires_clarification",
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content=clarification.model_dump_json(),
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content_type="json",
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metadata={
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"original_request": (
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request.model_dump()
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if hasattr(request, "model_dump")
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else str(request)
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)
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},
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)
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except (json.JSONDecodeError, ValueError, TypeError):
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# Not a JSON clarification request, treat as normal response
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pass
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# Normal text response - format using tool-specific formatting
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formatted_content = self.format_response(raw_text, request)
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# Determine content type based on the formatted content
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content_type = (
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"markdown"
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if any(
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marker in formatted_content for marker in ["##", "**", "`", "- ", "1. "]
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)
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else "text"
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)
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return ToolOutput(
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status="success",
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content=formatted_content,
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content_type=content_type,
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metadata={"tool_name": self.name},
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)
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@abstractmethod
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async def prepare_prompt(self, request) -> str:
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111
tools/chat.py
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111
tools/chat.py
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@@ -0,0 +1,111 @@
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"""
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Chat tool - General development chat and collaborative thinking
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"""
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from typing import Any, Dict, List, Optional
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from pydantic import Field
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from config import MAX_CONTEXT_TOKENS, TEMPERATURE_BALANCED
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from prompts import CHAT_PROMPT
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from utils import check_token_limit, read_files
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from .base import BaseTool, ToolRequest
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class ChatRequest(ToolRequest):
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"""Request model for chat tool"""
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prompt: str = Field(
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...,
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description="Your question, topic, or current thinking to discuss with Gemini",
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)
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context_files: Optional[List[str]] = Field(
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default_factory=list, description="Optional files for context"
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)
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class ChatTool(BaseTool):
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"""General development chat and collaborative thinking tool"""
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def get_name(self) -> str:
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return "chat"
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def get_description(self) -> str:
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return (
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"GENERAL CHAT & COLLABORATIVE THINKING - Use Gemini as your thinking partner! "
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"Perfect for: bouncing ideas during your own analysis, getting second opinions on your plans, "
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"collaborative brainstorming, validating your checklists and approaches, exploring alternatives. "
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"Also great for: explanations, comparisons, general development questions. "
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"Triggers: 'ask gemini', 'brainstorm with gemini', 'get gemini's opinion', 'discuss with gemini', "
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"'share my thinking with gemini', 'explain', 'what is', 'how do I'."
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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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"prompt": {
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"type": "string",
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"description": "Your question, topic, or current thinking to discuss with Gemini",
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},
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"context_files": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional files for context",
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},
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"temperature": {
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"type": "number",
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"description": "Response creativity (0-1, default 0.5)",
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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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},
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},
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"required": ["prompt"],
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}
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def get_system_prompt(self) -> str:
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return CHAT_PROMPT
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def get_default_temperature(self) -> float:
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return TEMPERATURE_BALANCED
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def get_request_model(self):
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return ChatRequest
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async def prepare_prompt(self, request: ChatRequest) -> str:
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"""Prepare the chat prompt with optional context files"""
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user_content = request.prompt
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# Add context files if provided
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if request.context_files:
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file_content, _ = read_files(request.context_files)
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user_content = f"{request.prompt}\n\n=== CONTEXT FILES ===\n{file_content}\n=== END CONTEXT ==="
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# Check token limits
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within_limit, estimated_tokens = check_token_limit(user_content)
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if not within_limit:
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raise ValueError(
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f"Content too large (~{estimated_tokens:,} tokens). "
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f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens."
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)
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# Combine system prompt with user content
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full_prompt = f"""{self.get_system_prompt()}
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=== USER REQUEST ===
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{user_content}
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=== END REQUEST ===
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Please provide a thoughtful, comprehensive response:"""
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return full_prompt
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def format_response(self, response: str, request: ChatRequest) -> str:
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"""Format the chat response (no special formatting needed)"""
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return response
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59
tools/models.py
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59
tools/models.py
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@@ -0,0 +1,59 @@
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"""
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Data models for tool responses and interactions
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"""
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from pydantic import BaseModel, Field
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from typing import Literal, Optional, Dict, Any, List
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class ToolOutput(BaseModel):
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"""Standardized output format for all tools"""
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status: Literal["success", "error", "requires_clarification"] = "success"
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content: str = Field(..., description="The main content/response from the tool")
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content_type: Literal["text", "markdown", "json"] = "text"
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metadata: Optional[Dict[str, Any]] = Field(default_factory=dict)
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class ClarificationRequest(BaseModel):
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"""Request for additional context or clarification"""
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question: str = Field(..., description="Question to ask Claude for more context")
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files_needed: Optional[List[str]] = Field(
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default_factory=list, description="Specific files that are needed for analysis"
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)
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suggested_next_action: Optional[Dict[str, Any]] = Field(
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None,
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description="Suggested tool call with parameters after getting clarification",
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)
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class DiagnosticHypothesis(BaseModel):
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"""A debugging hypothesis with context and next steps"""
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rank: int = Field(..., description="Ranking of this hypothesis (1 = most likely)")
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confidence: Literal["high", "medium", "low"] = Field(
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..., description="Confidence level"
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)
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hypothesis: str = Field(..., description="Description of the potential root cause")
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reasoning: str = Field(..., description="Why this hypothesis is plausible")
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next_step: str = Field(
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..., description="Suggested action to test/validate this hypothesis"
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)
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class StructuredDebugResponse(BaseModel):
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"""Enhanced debug response with multiple hypotheses"""
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summary: str = Field(..., description="Brief summary of the issue")
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hypotheses: List[DiagnosticHypothesis] = Field(
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..., description="Ranked list of potential causes"
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)
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immediate_actions: List[str] = Field(
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default_factory=list,
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description="Immediate steps to take regardless of root cause",
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)
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additional_context_needed: Optional[List[str]] = Field(
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default_factory=list,
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description="Additional files or information that would help with analysis",
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)
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