LLM Router MCP

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

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✨ 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
                            ├── SCAFFOLDING  → Gemini
                            ├── CODEGEN      → Claude
                            ├── REFACTOR     → Claude
                            ├── REVIEW       → Claude
                            ├── TESTING      → GPT-4o
                            └── IMPLEMENT    → GPT-4o

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🚀 Features

• Smart intent-based routing — classifies prompts into 8 task categories and dispatches to the optimal model
• Three frontier models — integrates Claude (Anthropic), Gemini (Google), and GPT-4o (OpenAI) out of the box
• Zero-config mock mode — works out of the box with no API keys; auto-enables mock mode when keys are absent
• Session-aware context caching — maintains conversation context across turns within the same session
• Explicit tool shortcuts — bypass auto-routing with dedicated plan_workflow, generate_code, and implement_feature tools
• MCP-native — drop it into any MCP-compatible host (Claude Desktop, Cursor, VS Code Continue, etc.)

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📦 Quick Start

Option A — npx (no install needed)

npx llm-router-mcp

Option B — Global install

npm install -g llm-router-mcp
llm-router-mcp

Option C — From source

git clone https://github.com/Devatva24/LLM-Router-MCP.git
cd LLM-Router-MCP
npm install
npm run build
node dist/index.js

No API keys? No problem. The server automatically falls back to mock mode and logs a helpful message. You only need keys when you want real model responses.

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🗺️ Routing Logic

Task Category	Trigger Keywords	Routed To
Planning	architecture, design, system design, strategy, workflow	✦ Gemini
Scaffolding	scaffold, boilerplate, setup, folder structure	✦ Gemini
Code Generation	write a function, implement, create a class, algorithm	✦ Claude
Refactor	refactor, clean up, improve, rewrite, restructure	✦ Claude
Code Review	review, debug, explain, what's wrong, critique	✦ Claude
Testing	unit tests, Jest, Vitest, test suite, test cases	✦ GPT-4o
Implementation	implement, add endpoint, build the, create the API	✦ GPT-4o
General	anything else	✦ Claude (fallback)

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⚙️ Configuration

Set your API keys as environment variables to use real models:

# Mac / Linux
export ANTHROPIC_API_KEY=sk-ant-...
export GOOGLE_API_KEY=AIza...
export OPENAI_API_KEY=sk-...

# Windows (PowerShell)
$env:ANTHROPIC_API_KEY="sk-ant-..."
$env:GOOGLE_API_KEY="AIza..."
$env:OPENAI_API_KEY="sk-..."

If any keys are missing the server auto-enables mock mode — no crash, no config needed.

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🖥️ Editor Integration

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "llm-router": {
      "command": "npx",
      "args": ["llm-router-mcp"]
    }
  }
}

Cursor

Add to your Cursor MCP settings:

{
  "mcpServers": {
    "llm-router": {
      "command": "npx",
      "args": ["llm-router-mcp"]
    }
  }
}

VS Code (Continue extension)

Add to ~/.continue/config.json:

{
  "mcpServers": {
    "llm-router": {
      "command": "npx",
      "args": ["llm-router-mcp"]
    }
  }
}

Ready-made config files for Cursor and Continue are included in the cursor-config/ and %USERPROFILE%/.continue/ directories of this repo.

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🧰 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"
}

generate_code

Explicitly routes to Claude for complex logic, algorithms, and refactoring.

{
  "prompt": "Write a binary search tree with insert and delete",
  "session_id": "my-session"
}

implement_feature

Explicitly routes to GPT-4o for feature implementation and test generation.

{
  "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"
}

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🧪 Running Tests

The test suite validates all routing decisions in mock mode — no API keys needed:

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)

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📁 Project Structure

LLM-Router-MCP/
├── src/
│   ├── index.ts          # MCP server entrypoint & routing logic
│   ├── classifier.ts     # Prompt intent classifier
│   ├── context-cache.ts  # Session-aware context management
│   ├── mock.ts           # Mock responses for zero-cost testing
│   └── logger.ts         # Lightweight logger
├── dist/                 # Compiled JavaScript (after npm run build)
├── cursor-config/        # Ready-made Cursor MCP config
├── test-router.cjs       # End-to-end test suite (mock mode)
├── tsconfig.json
└── package.json

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🤝 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

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📄 License

MIT — see LICENSE for details.