mcp-registry-search

lastmile-ai/mcp-registry-search

3.3

If you are the rightful owner of mcp-registry-search and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

The Model Context Protocol (MCP) server provides a semantic search API for MCP servers using a hybrid search approach combining BM25 and embeddings.

Tools
2
Resources
0
Prompts
0

MCP Registry Search

Semantic search API for MCP servers using the official Model Context Protocol registry.

Use as:

Cron job reindexes the entire registry every night.

Built with:

Usage

Search REST API

Query the registry using the /search endpoint:

Endpoint: https://mcp-registry-search.vercel.app/search?q=kubernetes&limit=2

Example:

curl "https://mcp-registry-search.vercel.app/search?q=kubernetes&limit=2"

Example Response:

{
  "results": [
    {
      "id": 259,
      "name": "io.github.vfarcic/dot-ai",
      "description": "AI-powered development platform for Kubernetes deployments and intelligent automation",
      "version": "0.101.0",
      "repository": {
        "url": "https://github.com/vfarcic/dot-ai",
        "source": "github"
      },
      "packages": [
        {
          "version": "0.101.0",
          "transport": {
            "type": "stdio"
          },
          "identifier": "@vfarcic/dot-ai",
          "registryType": "npm"
        }
      ],
      "remotes": [],
      "similarity_score": 0.606411385574579
    },
    {
      "id": 272,
      "name": "io.github.containers/kubernetes-mcp-server",
      "description": "An MCP server that provides [describe what your server does]",
      "version": "1.0.0",
      "repository": {
        "url": "https://github.com/containers/kubernetes-mcp-server",
        "source": "github"
      },
      "packages": [],
      "remotes": [],
      "similarity_score": 0.451448836663574
    }
  ],
  "query": "kubernetes",
  "limit": 2,
  "count": 2
}

Query Parameters:

  • q (required): Search query string
  • limit (optional): Maximum number of results (default: 10)
  • full_text_weight (optional): Weight for full-text search (default: 1.0)
  • semantic_weight (optional): Weight for semantic search (default: 1.0)

List Servers API

List all servers with pagination using the /servers endpoint:

Endpoint: https://mcp-registry-search.vercel.app/servers?limit=10&offset=0

Example:

curl "https://mcp-registry-search.vercel.app/servers?limit=5"

Query Parameters:

  • limit (optional): Maximum number of results (default: 100)
  • offset (optional): Number of results to skip (default: 0)

MCP Server

Connect to the MCP server via SSE for direct integration with MCP clients:

Endpoint: https://mcp-registry-search.vercel.app/api/sse

Available Tools:

  • search_mcp_servers(query, limit, full_text_weight, semantic_weight) - Search servers using hybrid search
  • list_mcp_servers(limit, offset) - List all servers with pagination

Add to your MCP client config:

{
  "mcpServers": {
    "registry-search": {
      "url": "https://mcp-registry-search.vercel.app/api/sse",
      "transport": {
        "type": "sse"
      }
    }
  }
}

Features

  • 🔍 Hybrid search combining lexical (PostgreSQL full-text) and semantic (pgvector) search
  • 🚀 Fast vector similarity using OpenAI embeddings + Supabase pgvector
  • 📊 Ranked results using weighted scoring
  • 🔄 Automatic ETL pipeline to fetch and index MCP servers
  • 🌐 FastAPI REST API for web access
  • 🔌 FastMCP server for MCP client integration
  • ☁️ Deployable to Vercel (FastAPI) and any MCP-compatible host (FastMCP)

Architecture

┌─────────────────┐
│  MCP Registry   │
│   (Source API)  │
└────────┬────────┘
         │
         │ ETL Pipeline
         ↓
┌─────────────────┐
│    Supabase     │
│  (PostgreSQL +  │
│    pgvector)    │
└────────┬────────┘
         │
         │ Search Query
         ↓
┌─────────────────┬─────────────────┐
│   FastAPI REST  │mcp-agent Server │
│      (Web)      │   (MCP Clients) │
└─────────────────┴─────────────────┘

Development

1. Install dependencies

uv sync

2. Set up Supabase

  1. Create a Supabase account
  2. Create a new project
  3. Run the SQL in schema.sql in the Supabase SQL editor
  4. Get your project URL and anon key from Settings > API

3. Configure environment variables

cp .env.example .env

Edit .env and add:

  • OPENAI_API_KEY: Your OpenAI API key
  • SUPABASE_URL: Your Supabase project URL
  • SUPABASE_KEY: Your Supabase anon key

4. Run ETL to fetch and index servers

uv run etl.py

This will:

  1. Fetch all servers from the MCP registry
  2. Filter to active servers (latest versions only)
  3. Generate embeddings using OpenAI
  4. Store in Supabase with vector indices

5. Start the servers

FastAPI (REST API):

uvicorn api:app --reload

API available at http://localhost:8000

  • Docs: http://localhost:8000/docs

FastMCP (MCP Server):

uv run main.py

API Usage

REST API

Search servers:

curl "http://localhost:8000/search?q=kubernetes&limit=5"

List all servers:

curl "http://localhost:8000/servers?limit=100&offset=0"

Health check:

curl "http://localhost:8000/health"

MCP Server

The FastMCP server provides:

Tools:

  • search_mcp_servers(query, limit, full_text_weight, semantic_weight) - Search servers
  • list_mcp_servers(limit, offset) - List all servers

Resources:

  • mcp-registry://search/{query} - Search results as formatted text

Prompts:

  • find_mcp_server(task) - Prompt template to find servers for a task

Add to your MCP client config:

{
  "mcpServers": {
    "registry-search": {
      "command": "uv",
      "args": ["run", "main.py"],
      "cwd": "/path/to/mcp-registry-search",
      "env": {
        "OPENAI_API_KEY": "your-key",
        "SUPABASE_URL": "your-url",
        "SUPABASE_KEY": "your-key"
      }
    }
  }
}

Deployment

Vercel (FastAPI)

  1. Install Vercel CLI:
npm i -g vercel
  1. Add environment variables to Vercel:
vercel env add OPENAI_API_KEY
vercel env add SUPABASE_URL
vercel env add SUPABASE_KEY
vercel env add CRON_SECRET  # Random secret to protect cron endpoint
  1. Deploy:
vercel

Automatic ETL Updates: The project includes a Vercel Cron job that runs nightly at midnight (UTC) to refresh the server index. The cron job calls /api/cron/etl which is protected by the CRON_SECRET environment variable.

Public SSE Proxy (Edge)

Expose an authenticated upstream SSE endpoint publicly by proxying through a Vercel Edge Function that injects the bearer token and streams responses.

  1. Configure env vars (in Vercel):
vercel env add UPSTREAM_SSE_URL   # e.g. https://<host>/sse
vercel env add UPSTREAM_SSE_TOKEN # bearer token for upstream

Alternative names supported for the token: LM_API_KEY or LM_API_TOKEN.

  1. Endpoint path
  • The SSE proxy is available at /api/sse (see api/sse.ts).
  • The MCP messages proxy is available at /api/messages (see api/messages.ts).
  • Rewrites expose root paths: /sse → /api/sse, /messages → /api/messages for MCP clients that expect root-level endpoints.
  1. CORS and streaming
  • CORS: Access-Control-Allow-Origin: *
  • Streaming: Edge Runtime streams SSE by default; cache disabled.
  1. Example usage
curl -N https://<your-project>.vercel.app/api/sse
# or using the root rewrite
curl -N https://<your-project>.vercel.app/sse
  1. Custom upstream per deployment (optional)
  • Override with UPSTREAM_SSE_URL in env without changing code.
  • Messages upstream auto-derives from the SSE URL, or set UPSTREAM_MESSAGES_URL explicitly if needed.

mcp-agent Server

We use mcp-agent cloud to deploy and host the MCP server. Under the covers, it's a FastMCP server (see ).

To do so yourself, you can run:

  • uv run mcp-agent login
  • uv run mcp-agent deploy

Manual ETL Updates

To manually refresh the server index:

Locally:

uv run etl.py
# or
make etl

On Vercel (trigger cron endpoint):

curl -X GET https://your-project.vercel.app/api/cron/etl \
  -H "Authorization: Bearer YOUR_CRON_SECRET"

The automatic nightly cron job handles updates, but you can manually trigger it anytime.

Development

Project structure:

registry/
├── api.py              # FastAPI REST API (hosted on Vercel)
├── main.py             # MCP server (hosted on mcp-agent cloud)
├── search.py           # Search engine
├── etl.py              # ETL pipeline
├── schema.sql          # Supabase schema
├── pyproject.toml      # Dependencies
├── vercel.json         # Vercel config
└── README.md           # This file

License

Apache 2.0