Multi-LLM-MCP

An MCP server that lets any IDE agent delegate coding tasks to any LLM — cloud APIs or local models — through a single unified interface.

Instead of being locked into one model, your coding agent can call NVIDIA NIM, OpenRouter, Groq, DeepSeek, or a local Ollama model for a second opinion, code review, or specialized task.

- 5 providers out of the box— NVIDIA NIM, OpenRouter, Groq, DeepSeek, Ollama
- Local model support— Use Ollama for fully offline, private coding assistance
- Fine-grained control— Settemperature,max_tokens, andsystem_promptper call
- Secure by design— API keys stay in environment variables, never in code
- Connection pooling— Clients are cached for fast, efficient API calls
- MCP standard— Works with any MCP-compatible IDE (Claude Desktop, VS Code, Cursor, Windsurf, etc.)

git clone https://github.com/arjunkr303/multi-llm-mcp.git cd multi-llm-mcp python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt

Edit.envand add your API keys. You only need keys for the providers you want to use. Ollama requires no API key.

NVIDIA_API_KEY=your_nvidia_key_here OPENROUTER_API_KEY=your_openrouter_key_here GROQ_API_KEY=your_groq_key_here DEEPSEEK_API_KEY=your_deepseek_key_here

Claude Desktop(claude_desktop_config.json):

{ "mcpServers": { "multi-llm-gateway": { "command": "/path/to/multi-llm-mcp/venv/bin/python", "args": ["/path/to/multi-llm-mcp/server.py"] } } }

VS Code / Cursor(.vscode/mcp.jsonor IDE MCP settings):

{ "mcpServers": { "multi-llm-gateway": { "command": "/path/to/multi-llm-mcp/venv/bin/python", "args": ["/path/to/multi-llm-mcp/server.py"] } } }

Replace/path/to/multi-llm-mcpwith the actual path where you cloned the repo.

Once connected, your IDE agent has access to theask_llmtool. You can invoke it by prompting your agent naturally. The agent will call the MCP server behind the scenes.

Delegates a coding question, code review, or rewrite task to an external LLM.

Use these prompts in your IDE chat to trigger the MCP tool:

Use NVIDIA's Llama 3.1 to rewrite the database query in db.py to use async/await.

Ask Groq's Llama 3 to review server.py for performance issues and suggest improvements.

Use Ollama to refactor the authentication logic in auth.py into smaller, testable functions.

Ask DeepSeek Coder to write unit tests for the parse_config function in utils.py.

Use OpenRouter's Claude to evaluate whether the current project structure follows best practices and suggest a better layout.

For fully private, offline coding assistance:

# Install Ollama: https://ollama.com/download ollama pull llama3 ollama pull codellama

Then useprovider: "ollama"with any pulled model name. No API key needed.

- API keys are loaded from environment variables only
- .envis gitignored and never committed
- No secrets are hardcoded in source code
- All API communication happens server-side only

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