
MindBridge MCP Server
An AI router that connects applications to multiple LLM providers (OpenAI, Anthropic, Google, DeepSeek, Ollama, etc.) with smart model orchestration capabilities, enabling dynamic switching between models for different reasoning tasks.
Tools
listProviders
List all configured LLM providers and their available models
listReasoningModels
List all available models that support reasoning capabilities
getSecondOpinion
Get responses from various LLM providers
README
<p align="center"> <img src="https://res.cloudinary.com/di7ctlowx/image/upload/v1744269194/logo_ghalxq.png" alt="Mindbridge Logo" width="400"> </p>
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
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 keyANTHROPIC_API_KEY
: Your Anthropic API keyDEEPSEEK_API_KEY
: Your DeepSeek API keyGOOGLE_API_KEY
: Your Google AI API keyOPENROUTER_API_KEY
: Your OpenRouter API keyOLLAMA_BASE_URL
: Ollama instance URL (default: http://localhost:11434)OPENAI_COMPATIBLE_API_KEY
: (Optional) API key for OpenAI-compatible servicesOPENAI_COMPATIBLE_API_BASE_URL
: Base URL for OpenAI-compatible servicesOPENAI_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
}
Development 🔧
npm run lint
: Run ESLintnpm run format
: Format code with Prettiernpm run clean
: Clean build artifactsnpm 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
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