Semantic MCP

Semantic router for MCP ecosystems - Discover, manage, and execute tools across multiple MCP servers with progressive disclosure.

Overview

semantic-mcp is a FastMCP-based MCP server that provides semantic discovery and lifecycle management for other MCP servers. It connects to a discovery service for semantic search and manages server lifecycles locally via ZMQ-based IPC.

LLM Client (Claude/Cline)
    │ MCP Protocol
    ▼
┌─────────────────────────────┐
│       semantic-mcp          │
│    (FastMCP MCP Server)     │
├─────────────────────────────┤
│  Discovery → Semantic API   │
│  Execution → ZMQ + Sessions │
└─────────────────────────────┘
    │               │
    ▼               ▼
Discovery       MCP Servers
Service         (stdio/http)

Installation

Option 1: uvx (Recommended)

uvx semantic-mcp serve --transport stdio

Option 2: pip/uv

# Install from PyPI
pip install semantic-mcp

# Or with uv
uv pip install semantic-mcp

# Run
semantic-mcp serve --transport stdio

Option 3: Docker

docker pull milkymap/semantic-mcp:0.2

docker run -d \
  -p 8001:8001 \
  -e DISCOVERY_URL=http://your-discovery-service \
  -e DISCOVERY_API_KEY=your-key \
  milkymap/semantic-mcp:0.2 serve --transport streamable-http --port 8001

Option 4: From source

git clone https://github.com/milkymap/mcp_runtime
cd mcp_runtime
uv sync
uv run semantic-mcp serve

Configuration

Environment Variables

Variable	Description	Default
DISCOVERY_URL	Discovery service API URL	http://localhost:8000
DISCOVERY_API_KEY	API key for discovery authentication	None
DISCOVERY_ENCRYPTION_KEY	Key to decrypt sensitive env vars in server configs	None
TOOL_OFFLOADED_DATA_PATH	Path for large result offloading	/tmp/mcp_offloaded
MAX_RESULT_TOKENS	Max tokens before content offloading	4096
BACKGROUND_QUEUE_SIZE	Max background tasks in queue	100
OPENAI_API_KEY	OpenAI API key (for image descriptions)	None

MCP Client Integration

Claude Code / Cline (uvx)

Add to your .mcp.json or MCP config:

{
  "mcpServers": {
    "semantic-mcp": {
      "command": "uvx",
      "args": ["semantic-mcp", "serve", "--transport", "stdio"],
      "env": {
        "DISCOVERY_URL": "https://your-discovery-service",
        "DISCOVERY_API_KEY": "your-api-key"
      }
    }
  }
}

Claude Desktop (Docker)

{
  "mcpServers": {
    "semantic-mcp": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "DISCOVERY_URL", "-e", "DISCOVERY_API_KEY",
        "--add-host=host.docker.internal:host-gateway",
        "milkymap/semantic-mcp:0.2", "serve", "--transport", "stdio"
      ],
      "env": {
        "DISCOVERY_URL": "http://host.docker.internal:8000",
        "DISCOVERY_API_KEY": "your-key"
      }
    }
  }
}

Remote HTTP Server

Start the server:

semantic-mcp serve --transport streamable-http --host 0.0.0.0 --port 8001

Client configuration:

{
  "mcpServers": {
    "semantic-mcp": {
      "url": "http://your-server:8001/mcp"
    }
  }
}

Available Operations

semantic-mcp exposes a single semantic_router tool with these operations:

Discovery (lightweight)

Operation	Description
search_tools	Search for tools using natural language
search_servers	Search for servers using natural language
list_servers	List all registered servers
get_server_tools	List tools on a server
get_statistics	Get server/tool counts

Exploration (full details)

Operation	Description
get_server_info	Get detailed server information
get_tool_details	Get full tool schema and description

Lifecycle

Operation	Description
manage_server	Start or shutdown a server
list_running_servers	List currently running servers

Execution

Operation	Description
execute_tool	Execute a tool on a running server
poll_task_result	Check background task status
cancel_task	Cancel a running background task
list_tasks	List all background tasks
get_content	Retrieve offloaded content by reference ID

Workflow

1. DISCOVER    search_tools("your need")         → Find relevant tools
       ↓
2. EXPLORE     get_server_info(server)           → Check capabilities
               get_server_tools(server)          → List available tools
       ↓
3. UNDERSTAND  get_tool_details(server, tool)    → Get full schema (REQUIRED)
       ↓
4. START       manage_server(server, "start")    → Start the MCP server
       ↓
5. EXECUTE     execute_tool(server, tool, args)  → Run the tool
       ↓
6. CLEANUP     manage_server(server, "shutdown") → Stop when done (optional)

Important rules:

• Always call get_tool_details before execute_tool to understand the schema
• Always call manage_server(start) before executing tools
• Use in_background=true for long-running operations, then poll_task_result
• Large responses are automatically offloaded; use get_content(ref_id) to retrieve

Architecture

Component	Description
RuntimeEngine	Core runtime managing ZMQ communication and server lifecycle
DiscoveryClient	HTTP client for discovery service API
ContentManager	Large result offloading (text chunking, images)
BackgroundTasks	Priority queue for async tool execution
FastMCP	MCP server framework exposing tools to LLMs

Development

# Install with dev dependencies
uv sync --group dev

# Run tests
uv run pytest tests/ -v

License

MIT