Mindbridge Mcp

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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 🔥

What it does	Why you should use it
Multi-LLM Support	Instantly switch between OpenAI, Anthropic, Google, DeepSeek, OpenRouter, Ollama (local models), and OpenAI-compatible APIs.
Reasoning Engine Aware	Smart routing to models built for deep reasoning like Claude, GPT-4o, DeepSeek Reasoner, etc.
getSecondOpinion Tool	Ask multiple models the same question to compare responses side-by-side.
OpenAI-Compatible API Layer	Drop MindBridge into any tool expecting OpenAI endpoints (Azure, Together.ai, Groq, etc.).
Auto-Detects Providers	Just add your keys. MindBridge handles setup & discovery automagically.
Flexible as Hell	Configure everything via env vars, MCP config, or JSON — it’s your call.

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

Clone the repository:

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

Install dependencies:
```
chmod +x install.sh
./install.sh
```
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

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
}

listProviders
- Lists all configured providers and their available models
- No parameters required
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
}