What is Mindbridge Mcp

MindBridge is an AI orchestration MCP server that enables any application to communicate with various large language models (LLMs) such as OpenAI, Anthropic, DeepSeek, and Ollama through a single unified API.

Use cases

Use cases for MindBridge include building smarter AI development environments, creating LLM-powered backends, orchestrating AI models for complex reasoning tasks, and providing second opinions by querying multiple models.

How to use

To use MindBridge, install it via npm with the command ‘npm install -g @pinkpixe’. After installation, configure your API keys and start routing queries between different LLMs effortlessly.

Key features

Key features include multi-LLM support, a reasoning engine aware for smart routing, a getSecondOpinion tool for comparing model responses, an OpenAI-compatible API layer, automatic provider detection, and flexible configuration options.

Where to use

MindBridge can be used in various fields such as AI development, agent building, multi-model workflows, reasoning-heavy tasks, and any environment requiring seamless integration of multiple LLMs.

MindBridge MCP Server ⚡ The AI Router for Big Brain Moves

MindBridge is your AI command hub — a Model Context Protocol (MCP) server built to unify, organize, and supercharge your LLM workflows.

Forget vendor lock-in. Forget juggling a dozen APIs.
MindBridge connects your apps to any model, from OpenAI and Anthropic to Ollama and DeepSeek — and lets them talk to each other like a team of expert consultants.

Need raw speed? Grab a cheap model.
Need complex reasoning? Route it to a specialist.
Want a second opinion? MindBridge has that built in.

This isn’t just model aggregation. It’s model orchestration.

Core Features 🔥

Why MindBridge?

“Every LLM is good at something. MindBridge makes them work together.”

Perfect for:

• Agent builders
• Multi-model workflows
• AI orchestration engines
• Reasoning-heavy tasks
• Building smarter AI dev environments
• LLM-powered backends
• Anyone tired of vendor walled gardens

Installation 🛠️

Option 1: Install from npm (Recommended)

# Install globally
npm install -g @pinkpixel/mindbridge

# use with npx
npx @pinkpixel/mindbridge

Installing via Smithery

To install mindbridge-mcp for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @pinkpixel-dev/mindbridge-mcp --client claude

Option 2: Install from source

1. Clone the repository:

   git clone https://github.com/pinkpixel-dev/mindbridge.git
   cd mindbridge
   

2. Install dependencies:

   chmod +x install.sh
   ./install.sh
   

3. Configure environment variables:

   cp .env.example .env
   

   Edit .env and add your API keys for the providers you want to use.

Configuration ⚙️

Environment Variables

The server supports the following environment variables:

• OPENAI_API_KEY: Your OpenAI API key
• ANTHROPIC_API_KEY: Your Anthropic API key
• DEEPSEEK_API_KEY: Your DeepSeek API key
• GOOGLE_API_KEY: Your Google AI API key
• OPENROUTER_API_KEY: Your OpenRouter API key
• OLLAMA_BASE_URL: Ollama instance URL (default: http://localhost:11434)
• OPENAI_COMPATIBLE_API_KEY: (Optional) API key for OpenAI-compatible services
• OPENAI_COMPATIBLE_API_BASE_URL: Base URL for OpenAI-compatible services
• OPENAI_COMPATIBLE_API_MODELS: Comma-separated list of available models

MCP Configuration

For use with MCP-compatible IDEs like Cursor or Windsurf, you can use the following configuration in your mcp.json file:

{
  "mcpServers": {
    "mindbridge": {
      "command": "npx",
      "args": [
        "-y",
        "@pinkpixel/mindbridge"
      ],
      "env": {
        "OPENAI_API_KEY": "OPENAI_API_KEY_HERE",
        "ANTHROPIC_API_KEY": "ANTHROPIC_API_KEY_HERE",
        "GOOGLE_API_KEY": "GOOGLE_API_KEY_HERE",
        "DEEPSEEK_API_KEY": "DEEPSEEK_API_KEY_HERE",
        "OPENROUTER_API_KEY": "OPENROUTER_API_KEY_HERE"
      },
      "provider_config": {
        "openai": {
          "default_model": "gpt-4o"
        },
        "anthropic": {
          "default_model": "claude-3-5-sonnet-20241022"
        },
        "google": {
          "default_model": "gemini-2.0-flash"
        },
        "deepseek": {
          "default_model": "deepseek-chat"
        },
        "openrouter": {
          "default_model": "openai/gpt-4o"
        },
        "ollama": {
          "base_url": "http://localhost:11434",
          "default_model": "llama3"
        },
        "openai_compatible": {
          "api_key": "API_KEY_HERE_OR_REMOVE_IF_NOT_NEEDED",
          "base_url": "FULL_API_URL_HERE",
          "available_models": [
            "MODEL1",
            "MODEL2"
          ],
          "default_model": "MODEL1"
        }
      },
      "default_params": {
        "temperature": 0.7,
        "reasoning_effort": "medium"
      },
      "alwaysAllow": [
        "getSecondOpinion",
        "listProviders",
        "listReasoningModels"
      ]
    }
  }
}

Replace the API keys with your actual keys. For the OpenAI-compatible configuration, you can remove the api_key field if the service doesn’t require authentication.

Usage 💫

Starting the Server

Development mode with auto-reload:

npm run dev

Production mode:

npm run build
npm start

When installed globally:

mindbridge

Available Tools

1. getSecondOpinion

   {
     provider: string;  // LLM provider name
     model: string;     // Model identifier
     prompt: string;    // Your question or prompt
     systemPrompt?: string;  // Optional system instructions
     temperature?: number;   // Response randomness (0-1)
     maxTokens?: number;    // Maximum response length
     reasoning_effort?: 'low' | 'medium' | 'high';  // For reasoning models
   }
   

2. listProviders

   • Lists all configured providers and their available models
   • No parameters required

3. listReasoningModels

   • Lists models optimized for reasoning tasks
   • No parameters required

Example Usage 📝

// Get an opinion from GPT-4o
{
  "provider": "openai",
  "model": "gpt-4o",
  "prompt": "What are the key considerations for database sharding?",
  "temperature": 0.7,
  "maxTokens": 1000
}

// Get a reasoned response from OpenAI's o1 model
{
  "provider": "openai",
  "model": "o1",
  "prompt": "Explain the mathematical principles behind database indexing",
  "reasoning_effort": "high",
  "maxTokens": 4000
}

// Get a reasoned response from DeepSeek
{
  "provider": "deepseek",
  "model": "deepseek-reasoner",
  "prompt": "What are the tradeoffs between microservices and monoliths?",
  "reasoning_effort": "high",
  "maxTokens": 2000
}

// Use an OpenAI-compatible provider
{
  "provider": "openaiCompatible",
  "model": "YOUR_MODEL_NAME",
  "prompt": "Explain the concept of eventual consistency in distributed systems",
  "temperature": 0.5,
  "maxTokens": 1500
}

Development 🔧

• npm run lint: Run ESLint
• npm run format: Format code with Prettier
• npm run clean: Clean build artifacts
• npm run build: Build the project

Contributing

PRs welcome! Help us make AI workflows less dumb.

License

MIT — do whatever, just don’t be evil.

Made with ❤️ by Pink Pixel