Pinecone Agentic Search MCP Server

Node.js · TypeScript · Pinecone · OpenRouter · SSE · Railway

A custom MCP (Model Context Protocol) server that exposes Pinecone vector search as a standardized tool for AI agents — searching 4,128 embedded ArXiv research papers covering AI Agents, RAG, MCP, and Prompt Engineering.

Built as the infrastructure layer for GenAI Concepts Chat, replacing the original n8n MCP dependency with a custom server that gives full ownership of the MCP layer with no subscription dependencies.

🔗 Live Endpoint

https://pinecone-mcp-server-production-189c.up.railway.app/mcp

Health check:

https://pinecone-mcp-server-production-189c.up.railway.app/health

Related MCP server: arXiv Research MCP Server

Architecture

AI Agent / MCP Client
        │  POST /mcp  (SSE transport)
        ▼
  Express + MCP Server
        │
  ┌─────┴─────────────┐
  │   agentic-search  │
  └─────┬─────────────┘
        │
   OpenRouter (embeddings)    Pinecone (vector search)
   text-embedding-3-small     mcp-server-v1 / arxiv-papers

• Transport: SSE — legacy MCP transport

• Health check: GET /health — public, no auth required

• MCP endpoint: POST /mcp

How It Fits the Portfolio

This server is one of two custom MCP servers in this portfolio:

Pinecone Agentic Search MCP Server (this server)
  → SSE transport (legacy MCP pattern)
  → Single tool: agentic-search
  → Searches embedded ArXiv corpus via Pinecone
  → No LLM calls for search — pure vector similarity
  → Called by: GenAI Concepts Chat

Web Research Hub MCP Server
  → Streamable HTTP transport (current MCP spec standard)
  → 4 tools: web_search, fetch_url, calculate, export_report
  → Searches live web via Exa AI
  → Called by: Web Research Hub

Having both transport patterns (SSE and Streamable HTTP) in the same portfolio demonstrates understanding of the MCP protocol evolution, not just one implementation of it.

What Makes This Different from the Web Research Hub MCP Server

Tool Reference

agentic-search

Searches the embedded ArXiv research corpus via Pinecone vector similarity. Accepts a natural language query, embeds it using OpenAI text-embedding-3-small via OpenRouter, and returns the most relevant excerpts with relevance scores. Use when the query requires grounded academic or research paper context on AI Agents, RAG, MCP, or Prompt Engineering topics.

Response: Top-N relevant excerpts with relevance scores from the 4,128-paper ArXiv corpus.

Authentication

Every request except GET /health must include an X-API-Key header:

X-API-Key: your-secret-key

Missing or invalid keys return HTTP 401:

{
  "error": "Unauthorized.",
  "message": "This is a portfolio demonstration server. To use
  this tool, clone the repo and deploy your own instance with
  your own API keys: github.com/Paul-Orlando/pinecone-mcp-server"
}

Rate Limits

POST /mcp is limited to 5 requests per IP address per hour. Exceeding the limit returns HTTP 429. GET /health is not rate-limited.

Note: one agentic-search query may generate multiple internal /mcp requests. This limit allows approximately 3-4 complete queries per hour.

This is a portfolio demonstration server. To remove these limits, clone the repo and deploy your own instance with your own API keys.

Environment Variables

Generate a key with:

node -e "console.log(require('crypto').randomBytes(32).toString('hex'))"

Local Development

# 1. Install dependencies
npm install

# 2. Configure environment
cp .env.example .env
# Edit .env and add your keys:
# PINECONE_API_KEY, OPENROUTER_API_KEY, MCP_API_KEY

# 3. Run locally
npm run dev

Verify it's running:

curl http://localhost:3001/health

Deployment

Railway (recommended)

1. Push this repo to GitHub

2. New Project → Deploy from GitHub repo

3. Add environment variables: PINECONE_API_KEY, OPENROUTER_API_KEY, MCP_API_KEY

4. Railway auto-deploys from the Procfile

5. Settings → Networking → Generate Domain → Set Target Port to 3001

Connecting to Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "pinecone-agentic-search": {
      "url": "https://pinecone-mcp-server-production-189c.up.railway.app/mcp",
      "headers": {
        "X-API-Key": "your-secret-key"
      }
    }
  }
}

For local development use http://localhost:3001/mcp.

Data Source

4,128 ArXiv research papers covering:

• AI Agents

• Retrieval-Augmented Generation (RAG)

• Model Context Protocol (MCP)

• Prompt Engineering

Used for non-commercial demonstration purposes only. Papers are subject to their respective authors' licenses (CC BY 4.0).

Usage Note

This is a portfolio demonstration server with rate limiting and API key authentication. For production use, clone the repo and deploy your own instance with your own API keys — the deployment instructions above are included for exactly this purpose.

Roadmap

• Streamable HTTP transport — upgrade from SSE to match the current MCP spec standard

• Expand the corpus — add more research papers beyond the current 4,128 ArXiv papers

• Metadata filtering — filter by publication date, author, or topic category

• Tool call logging for observability

Evolution

This server replaces the n8n MCP server used in the original GenAI Concepts Chat architecture, giving full ownership of the MCP layer with no subscription dependencies. The transition from n8n-hosted MCP to a custom Node.js/TypeScript server is documented in the GenAI Concepts Chat repo.

Related Repos

Author

Paul Orlando Creative Technologist | AI Agent Developer | Data Analytics 🌐 paulforlando.com  |  💼 LinkedIn  |  🐙 GitHub

License

MIT License