MCP Registry Search
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Registry Searchsearch for servers related to cloud monitoring"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Registry Search
Semantic search API for MCP servers using the official Model Context Protocol registry.
Use as:
REST API (search): https://mcp-registry-search.vercel.app/search?q=kubernetes&limit=2
REST API (list): https://mcp-registry-search.vercel.app/servers?limit=10&offset=0
MCP Server (SSE): https://mcp-registry-search.vercel.app/api/sse
Cron job reindexes the entire registry every night.
Built with:
Supabase
Vercel
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 stringlimit(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 searchlist_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 sync2. Set up Supabase
Create a Supabase account
Create a new project
Run the SQL in
schema.sqlin the Supabase SQL editorGet your project URL and anon key from Settings > API
3. Configure environment variables
cp .env.example .envEdit .env and add:
OPENAI_API_KEY: Your OpenAI API keySUPABASE_URL: Your Supabase project URLSUPABASE_KEY: Your Supabase anon key
4. Run ETL to fetch and index servers
uv run etl.pyThis will:
Fetch all servers from the MCP registry
Filter to active servers (latest versions only)
Generate embeddings using OpenAI
Store in Supabase with vector indices
5. Start the servers
FastAPI (REST API):
uvicorn api:app --reloadAPI available at http://localhost:8000
Docs:
http://localhost:8000/docs
FastMCP (MCP Server):
uv run main.pyAPI 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 serverslist_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)
Install Vercel CLI:
npm i -g vercelAdd 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 endpointDeploy:
vercelAutomatic 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.
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 upstreamAlternative names supported for the token: LM_API_KEY or LM_API_TOKEN.
Endpoint path
The SSE proxy is available at
/api/sse(seeapi/sse.ts).The MCP messages proxy is available at
/api/messages(seeapi/messages.ts).Rewrites expose root paths:
/sse→/api/sse,/messages→/api/messagesfor MCP clients that expect root-level endpoints.
CORS and streaming
CORS:
Access-Control-Allow-Origin: *Streaming: Edge Runtime streams SSE by default; cache disabled.
Example usage
curl -N https://<your-project>.vercel.app/api/sse
# or using the root rewrite
curl -N https://<your-project>.vercel.app/sseCustom upstream per deployment (optional)
Override with
UPSTREAM_SSE_URLin env without changing code.Messages upstream auto-derives from the SSE URL, or set
UPSTREAM_MESSAGES_URLexplicitly 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 main.py).
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 etlOn 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 fileLicense
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