LLM Router MCP

Route prompts intelligently across Claude, Gemini, and GPT-4o — automatically picking the best model for every task while minimising token cost.

✨ What is this?

llm-router-mcp is a Model Context Protocol (MCP) server that acts as an intelligent dispatcher for your AI workloads. Instead of hardcoding a single LLM into your workflow, the router analyses the intent of each prompt and automatically selects the most cost-effective and capable model for that specific task type.

Your prompt ──► LLM Router ──► PLANNING  → Gemini
                            ├── CODEGEN  → Claude
                            ├── TESTING  → GPT-4o
                            └── REVIEW   → Claude

🚀 Features

• Smart intent-based routing — classifies prompts into task categories (planning, codegen, testing, review) and dispatches to the optimal model
• Three frontier models — integrates Claude (Anthropic), Gemini (Google), and GPT-4o (OpenAI) out of the box
• Session-aware context caching — maintains conversation context across turns within the same session, enabling multi-turn workflows
• Explicit tool shortcuts — bypass auto-routing with dedicated plan_workflow and implement_feature tools
• Mock mode — run the full server locally without API keys for testing and CI pipelines
• MCP-native — drop it into any MCP-compatible host (Claude Desktop, Cursor, VS Code Continue, etc.)

🗺️ Routing Logic

| Task Category | Trigger Keywords / Tool | Routed To | |---|---|---| | Planning | Architecture, design, system design, strategy | ✦ Gemini | | Code Generation | Write, implement, build, create a function | ✦ Claude | | Testing | Unit tests, Jest, test suite, test cases | ✦ GPT-4o | | Code Review | Review, what's wrong, analyse, critique | ✦ Claude | | plan_workflow tool | Explicit planning requests | ✦ Gemini | | implement_feature tool | Explicit implementation requests | ✦ GPT-4o |

📦 Installation

Prerequisites

• Node.js 20+
• API keys for the models you want to use

Clone & install

git clone https://github.com/Devatva24/llm-router.git
cd llm-router
npm install

Build

npm run build

⚙️ Configuration

Set your API keys as environment variables before starting the server:

export ANTHROPIC_API_KEY=sk-ant-...
export GOOGLE_API_KEY=AIza...
export OPENAI_API_KEY=sk-...

VS Code (Continue)

Add the following to your %USERPROFILE%/.continue/config.json (the cursor-config/ directory in this repo contains ready-made configs):

{
  "mcpServers": {
    "llm-router": {
      "command": "node",
      "args": ["path/to/llm-router/dist/index.js"]
    }
  }
}

Cursor

Follow the same pattern in your Cursor MCP settings, pointing args at the built dist/index.js.

🛠️ Usage

Start the server

npm start

Start in mock mode (no API keys needed)

npm run start:mock

Development mode (TypeScript, no build step)

npm run dev

🧰 Available MCP Tools

route_prompt

Automatically classifies and routes a prompt to the best model.

{
  "prompt": "Write a recursive function to flatten deeply nested objects",
  "session_id": "my-session"
}

plan_workflow

Explicitly routes to Gemini for high-level planning and architecture tasks.

{
  "prompt": "Design a checkout flow for an e-commerce app",
  "session_id": "my-session"
}

implement_feature

Explicitly routes to GPT-4o for feature implementation.

{
  "prompt": "Implement the /api/products CRUD endpoints",
  "session_id": "my-session"
}

clear_context

Clears the cached conversation context for a given session.

{
  "session_id": "my-session"
}

🧪 Running Tests

The test suite spins up the server in mock mode and validates routing decisions end-to-end:

npm test

Expected output:

🧪 LLM Router — mock test suite

  ✅ planning → Gemini
  ✅ codegen → Claude
  ✅ testing → GPT-4o
  ✅ review → Claude
  ✅ explicit plan_workflow
  ✅ explicit implement_feature
  ✅ context cache — 2nd turn
  ✅ clear_context

──────────────────────────────────────────
  8 passed  0 failed (8 total)

📁 Project Structure

llm-router/
├── src/                    # TypeScript source
│   └── index.ts            # MCP server entrypoint & routing logic
├── dist/                   # Compiled JavaScript (after npm run build)
├── cursor-config/          # Ready-made Cursor MCP config
├── %USERPROFILE%/.continue/# Ready-made Continue MCP config
├── test-router.cjs         # End-to-end test suite (mock mode)
├── tsconfig.json
└── package.json

🤝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request for:

• Adding support for additional LLM providers
• Improving routing classification accuracy
• Adding streaming response support
• Writing more test coverage

📄 License

MIT — see LICENSE for details.