μ-MCP Server

μ (mu) = micro, minimal - in sardonic contrast to zen-mcp's 10,000+ lines of orchestration.

A pure MCP server that does one thing well: enable chat with AI models via OpenRouter.

Philosophy

Following UNIX principles:

Do one thing well: Provide access to AI models
No hardcoded control flow: The AI agents decide everything
Minimal interface: One tool, clean parameters
Persistent state: Conversations persist across sessions
Model agnostic: Support any OpenRouter model

Features

✅ Multi-model conversations - Switch models mid-conversation
✅ Persistent storage - Conversations saved to disk
✅ Model registry - Curated models with capabilities
✅ LLM-driven model selection - Calling agent picks the best model
✅ Reasoning effort control - Simple pass-through to OpenRouter (low/medium/high)
✅ MCP prompts - Slash commands /mu:chat and /mu:continue
✅ Token-based budgeting - Smart file truncation
✅ Proper MIME types - Correct image format handling

What's NOT Included

❌ Workflow orchestration (let AI decide)
❌ Step tracking (unnecessary complexity)
❌ Confidence levels (trust the models)
❌ Expert validation (models are the experts)
❌ Hardcoded procedures (pure AI agency)
❌ Web search implementation (just ask Claude)
❌ Multiple providers (OpenRouter handles all)

Setup

Quick Install (with uv)

Get OpenRouter API key: https://openrouter.ai/keys
Run setup script:
```
./setup.sh
```
This will:
- Install uv if not present (blazing fast Python package manager)
- Install dependencies
- Show you the Claude Desktop config

Add to Claude Desktop config (~/.config/claude/claude_desktop_config.json):

{
  "mcpServers": {
    "mu-mcp": {
      "command": "uv",
      "args": ["--directory", "/path/to/mu-mcp", "run", "python", "/path/to/mu-mcp/server.py"],
      "env": {
        "OPENROUTER_API_KEY": "your-key-here"
      }
    }
  }
}

Restart Claude Desktop

Manual Install (traditional)

If you prefer pip/venv:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Then use this Claude Desktop config:

{
  "mcpServers": {
    "mu-mcp": {
      "command": "/path/to/mu-mcp/venv/bin/python",
      "args": ["/path/to/mu-mcp/server.py"],
      "env": {
        "OPENROUTER_API_KEY": "your-key-here"
      }
    }
  }
}

## Usage

### Basic Chat

/mu:chat Then specify model and prompt: "Use sonnet to explain quantum computing"


### Continue Conversations

/mu:continue Claude sees your recent conversations and can intelligently continue them Preserves full context even after Claude's memory is compacted


**Key Use Case**: Maintain context between different Claude sessions. When Claude's context gets compacted or you need to switch between tasks, `/mu:continue` allows Claude to see all your recent conversations (with titles, timestamps, and models used) and seamlessly resume where you left off. The agent intelligently selects or asks which conversation to continue based on your needs.

### Challenge Mode

/mu:challenge Encourages critical thinking and avoids reflexive agreement


### Multi-AI Discussion

/mu:discuss Orchestrate multi-turn discussions among diverse AI models


### Model Selection

Chat with Sonnet for code optimization Chat with Opus for deep analysis Chat with Haiku for quick edits Chat with O3 Mini High for complex reasoning Chat with Gemini 3 Flash for low-latency tasks Chat with Sonnet about API design


### Reasoning Effort Control

Chat with o3-mini using high reasoning effort for complex problems Chat with o3 using medium reasoning effort for balanced analysis Chat with o4-mini-high using medium reasoning effort for balanced analysis


Note: Reasoning effort is automatically ignored by models that don't support it.

### With Files and Images

Review this code: /path/to/file.py Analyze this diagram: /path/to/image.png


### Model Selection by LLM
The calling LLM agent (Claude) sees all available models with their descriptions and capabilities, allowing intelligent selection based on:
- Task requirements and complexity
- Performance vs cost trade-offs  
- Specific model strengths
- Context window needs
- Image support requirements

## Architecture

server.py # MCP server with prompt handlers chat_handler.py # Chat logic with multi-model support models.py # Model registry and capabilities prompts.py # System prompts for peer AI collaboration storage.py # Persistent conversation storage .env.example # Configuration template


## Configuration

### Environment Variables

- `OPENROUTER_API_KEY` - Your OpenRouter API key (required)
- `OPENROUTER_ALLOWED_MODELS` - Comma-separated list of allowed models (optional)
- `LOG_LEVEL` - Logging verbosity (DEBUG, INFO, WARNING, ERROR)

## Why μ-MCP?

### The Problem with zen-mcp-server

zen-mcp grew to **10,000+ lines** trying to control AI behavior:
- 15+ specialized tools with overlapping functionality
- Complex workflow orchestration that limits AI agency
- Hardcoded decision trees that prescribe solutions
- "Step tracking" and "confidence levels" that add noise
- Redundant schema fields and validation layers

### The μ-MCP Approach

**Less code, more capability**:
- Single tool that does one thing perfectly
- AI agents make all decisions
- Clean, persistent conversation state
- Model capabilities, not hardcoded behaviors
- Trust in AI intelligence over procedural control

### Philosophical Difference

**zen-mcp**: "Let me orchestrate 12 steps for you to debug this code"
**μ-mcp**: "Here's the model catalog. Pick what you need."

The best tool is the one that gets out of the way.

## Related Projects

- [zen-mcp-server](https://github.com/winnative/zen-mcp-server) - The bloated alternative we're reacting against

## License

MIT

PAL (Multi-Provider AI)