Deep Code Reasoning
Pairs Claude Code with Google's Gemini AI for complementary code analysis, enabling intelligent routing where Claude handles local-context operations while Gemini leverages its 1M token context for distributed system debugging and long-trace analysis
What is Deep Code Reasoning?
Deep Code Reasoning is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to pairs claude code with google's gemini ai for complementary code analysis, enabling intelligent routing where claude handles local-context operations while gemini leverages its 1m token context for di...
Pairs Claude Code with Google's Gemini AI for complementary code analysis, enabling intelligent routing where Claude handles local-context operations while Gemini leverages its 1M token context for distributed system debugging and long-trace analysis
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Pairs Claude Code with Google's Gemini AI for complementary
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx deep-code-reasoning-mcp-serverConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Deep Code Reasoning
Deep Code Reasoning MCP Server pairs Claude Code with Google's Gemini AI to enable complementary, multi-model code analysis. Claude handles local context and immediate code operations while Gemini's 1-million-token context window is leveraged for distributed system debugging, long execution trace analysis, and cross-service impact assessment. Developers use it to escalate complex debugging problems that exceed Claude's context window or benefit from a second AI perspective, enabling iterative Claude-Gemini dialogue sessions that produce structured analysis results.
Prerequisites
- Node.js 18+ and npm installed
- A Google Gemini API key obtained from Google AI Studio (makersuite.google.com)
- Claude Desktop or another MCP-compatible client installed
- Git to clone the repository
- Basic familiarity with TypeScript build tooling (npm run build)
Clone the repository
Clone the deep-code-reasoning-mcp repository to your local machine.
git clone https://github.com/evalops/deep-code-reasoning-mcp.git
cd deep-code-reasoning-mcpInstall dependencies
Install the required npm packages for the project.
npm installConfigure your Gemini API key
Copy the example environment file and add your Gemini API key. You can get a key for free from Google AI Studio.
cp .env.example .env
# Open .env and set:
# GEMINI_API_KEY=your-actual-api-key-hereBuild the TypeScript project
Compile the TypeScript source to JavaScript so it can be run by Node.js.
npm run buildAdd to Claude Desktop configuration
Edit your Claude Desktop config to register the server. Use the absolute path to the compiled dist/index.js file.
Verify the server loads
Restart Claude Desktop and check that the deep-code-reasoning tools appear. You should see tools like escalate_analysis, start_conversation, and trace_execution_path available.
Deep Code Reasoning Examples
Client configuration
Claude Desktop configuration for the deep-code-reasoning-mcp server. Replace /path/to with your actual clone location and add your Gemini API key.
{
"mcpServers": {
"deep-code-reasoning": {
"command": "node",
"args": ["/path/to/deep-code-reasoning-mcp/dist/index.js"],
"env": {
"GEMINI_API_KEY": "your-gemini-api-key-here"
}
}
}
}Prompts to try
Example prompts that trigger multi-model analysis via the available tools.
- "I've found multiple N+1 database queries in this service but can't determine if batching is safe. Can Gemini analyze the full execution trace and suggest optimizations?"
- "This microservice is failing intermittently under load. Start a conversation with Gemini to analyze these 5,000 lines of logs."
- "Trace the execution path of this API call through our distributed system and identify all service boundaries it crosses."
- "Test my hypothesis that the memory leak is caused by unclosed event listeners — use hypothesis_test to validate with evidence from the codebase."Troubleshooting Deep Code Reasoning
Server fails to start with 'GEMINI_API_KEY is not set' error
Ensure GEMINI_API_KEY is set in the env block of your Claude Desktop config JSON, not just in your .env file. The MCP server process does not automatically inherit shell environment variables.
Build fails with TypeScript compilation errors
Run `npm install` first to ensure all type definitions are present, then retry `npm run build`. Check that your Node.js version is 18 or higher with `node --version`.
Gemini returns rate limit errors during long analysis sessions
The free Gemini API tier has per-minute token limits. For large codebases, break the analysis into smaller chunks using start_conversation / continue_conversation rather than sending the entire codebase at once.
Frequently Asked Questions about Deep Code Reasoning
What is Deep Code Reasoning?
Deep Code Reasoning is a Model Context Protocol (MCP) server that pairs claude code with google's gemini ai for complementary code analysis, enabling intelligent routing where claude handles local-context operations while gemini leverages its 1m token context for distributed system debugging and long-trace analysis It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Deep Code Reasoning?
Follow the installation instructions on the Deep Code Reasoning GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Deep Code Reasoning?
Deep Code Reasoning works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Deep Code Reasoning free to use?
Yes, Deep Code Reasoning is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
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Set Up Deep Code Reasoning in Your Editor
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Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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