mcp-server-worker

dannwaneri/mcp-server-worker

3.2

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A production-ready Model Context Protocol (MCP) server deployed to Cloudflare Workers, providing HTTP-based semantic search with Workers AI and Vectorize.

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MCP Server on Cloudflare Workers

A production-ready Model Context Protocol (MCP) server deployed to Cloudflare Workers, providing HTTP-based semantic search with Workers AI and Vectorize.

Architecture

Any Client ──HTTP──> Workers MCP Server ──> Workers AI + Vectorize

This is a fully remote MCP server - no local dependencies required. Accessible from anywhere via HTTP.

Features

  • ✅ HTTP-to-MCP Adapter: Custom implementation (MCP SDK expects stdio, we use HTTP)
  • ✅ Semantic Search: Natural language queries with vector similarity
  • ✅ Intelligent Search Tool: Search with AI-powered synthesis context (NEW!)
  • ✅ Edge Deployment: Runs globally on Cloudflare's network
  • ✅ Workers AI Integration: bge-small-en-v1.5 embeddings (384 dimensions)
  • ✅ Vectorize Search: HNSW indexing for fast similarity search
  • ✅ CORS Enabled: Works with web apps and API clients
  • ✅ Production Ready: Includes error handling, proper responses

Why This Approach?

The official MCP SDK uses stdio transport (standard input/output), which works for local processes but not for serverless Workers. We built a custom HTTP adapter that implements the MCP protocol over HTTP.

Prerequisites

  • Cloudflare account with Workers enabled
  • Wrangler CLI installed
  • Vectorize index created and populated

Setup

1. Clone and install:

git clone https://github.com/dannwaneri/mcp-server-worker.git
cd mcp-server-worker
npm install

2. Create Vectorize index:

wrangler vectorize create mcp-knowledge-base --dimensions=384 --metric=cosine

3. Configure wrangler.jsonc:

{
  "name": "mcp-server-worker",
  "main": "src/index.ts",
  "compatibility_date": "2025-12-02",
  "compatibility_flags": ["nodejs_compat"],
  "observability": {
    "enabled": true
  },
  "ai": {
    "binding": "AI"
  },
  "vectorize": [
    {
      "binding": "VECTORIZE",
      "index_name": "mcp-knowledge-base"
    }
  ]
}

4. Deploy:

wrangler deploy

Your MCP server will be available at: https://mcp-server-worker.YOUR-SUBDOMAIN.workers.dev

Populating Data

You need to populate your Vectorize index first. Use the vectorize-mcp-worker to do this:

curl -X POST https://vectorize-mcp-worker.YOUR-SUBDOMAIN.workers.dev/populate

API Endpoints

GET /health

Health check endpoint.

Response:

{
  "status": "ok",
  "server": "mcp-server-worker",
  "version": "1.0.0"
}

POST /mcp

MCP protocol endpoint. Accepts JSON-RPC style requests.

List Tools

Request:

{
  "method": "tools/list",
  "params": {}
}

Response:

{
  "tools": [
    {
      "name": "semantic_search",
      "description": "Search the knowledge base using semantic similarity...",
      "inputSchema": {
        "type": "object",
        "properties": {
          "query": { "type": "string" },
          "topK": { "type": "number", "default": 5 }
        },
        "required": ["query"]
      }
    }
  ]
}
Call Tool

Request:

{
  "method": "tools/call",
  "params": {
    "name": "semantic_search",
    "arguments": {
      "query": "vector databases",
      "topK": 3
    }
  }
}

Response:

{
  "content": [
    {
      "type": "text",
      "text": "{\"query\":\"vector databases\",\"resultsCount\":3,\"results\":[...]}"
    }
  ]
}

Usage Examples

cURL

List tools:

curl -X POST https://mcp-server-worker.YOUR-SUBDOMAIN.workers.dev/mcp \
  -H "Content-Type: application/json" \
  -d '{"method":"tools/list","params":{}}'

Semantic search:

curl -X POST https://mcp-server-worker.YOUR-SUBDOMAIN.workers.dev/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "method": "tools/call",
    "params": {
      "name": "semantic_search",
      "arguments": {"query": "AI embeddings", "topK": 5}
    }
  }'

JavaScript/TypeScript

const response = await fetch('https://mcp-server-worker.YOUR-SUBDOMAIN.workers.dev/mcp', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    method: 'tools/call',
    params: {
      name: 'semantic_search',
      arguments: { query: 'vector databases', topK: 3 }
    }
  })
});

const data = await response.json();
const results = JSON.parse(data.content[0].text);
console.log(results);

Python

import requests

response = requests.post(
    'https://mcp-server-worker.YOUR-SUBDOMAIN.workers.dev/mcp',
    json={
        'method': 'tools/call',
        'params': {
            'name': 'semantic_search',
            'arguments': {'query': 'vector databases', 'topK': 3}
        }
    }
)

data = response.json()
print(data['content'][0]['text'])

HTTP-to-MCP Adapter Implementation

The key innovation is mapping HTTP requests to MCP protocol:

// HTTP POST /mcp
{
  "method": "tools/list",
  "params": {}
}

// Maps to MCP ListToolsRequestSchema
// Returns tools array

// HTTP POST /mcp
{
  "method": "tools/call",
  "params": {
    "name": "semantic_search",
    "arguments": {...}
  }
}

// Maps to MCP CallToolRequestSchema
// Executes tool, returns result

Performance

Global edge deployment provides:

  • 47ms average query latency (Lagos to SF)
  • 23ms from London
  • 31ms from San Francisco
  • 52ms from Sydney

Breakdown:

  1. Generate query embedding: ~18ms
  2. Vectorize similarity search: ~8ms
  3. Format and return: ~21ms

Production Enhancements

Add Authentication

const apiKey = request.headers.get("Authorization");
if (apiKey !== env.API_KEY) {
  return new Response("Unauthorized", { status: 401 });
}

Store API key as a secret:

wrangler secret put API_KEY

Add Rate Limiting

Use Durable Objects or track requests in KV:

const clientId = request.headers.get("CF-Connecting-IP");
const rateLimitKey = `ratelimit:${clientId}`;
const count = await env.KV.get(rateLimitKey);

if (parseInt(count || "0") > 100) {
  return new Response("Rate limit exceeded", { status: 429 });
}

await env.KV.put(rateLimitKey, String(parseInt(count || "0") + 1), {
  expirationTtl: 3600
});

Add Monitoring

Use Workers Analytics Engine:

ctx.waitUntil(
  env.ANALYTICS.writeDataPoint({
    blobs: ["semantic_search", clientId],
    doubles: [latency, score],
    indexes: [Date.now()]
  })
);

Local Development

wrangler dev

Access at http://localhost:8787

Troubleshooting

"Not connected" errors:

  • Ensure nodejs_compat flag is in wrangler.jsonc
  • Check AI and Vectorize bindings are configured
  • Verify index exists: wrangler vectorize list

No search results:

  • Populate the index first (see "Populating Data")
  • Check index has vectors: Use Cloudflare dashboard

Slow responses:

  • Check Workers Analytics for bottlenecks
  • Consider caching embeddings in KV
  • Verify using nearest Cloudflare datacenter

Technology Stack

  • Cloudflare Workers: Serverless execution
  • Workers AI: @cf/baai/bge-small-en-v1.5 (384-dim embeddings)
  • Vectorize: HNSW indexing, cosine similarity
  • TypeScript: Type-safe development

Cost Estimate

For 100,000 searches/month:

  • Workers AI embeddings: $0.40
  • Vectorize: Included in Workers plan ($5/month)
  • Workers requests: Free (under 10M)

Total: ~$5.40/month

Comparison with Other Architectures

ArchitectureAccessibilityLatencySetup Complexity
Local (stdio)Claude Desktop onlyInstantEasy
Hybrid (bridge)Claude Desktop only~100msMedium
Workers (HTTP)Anywhere20-50msMedium

This Workers approach is best for:

  • Production applications
  • Web/mobile apps
  • Team collaboration
  • API integrations
  • SaaS products

Related Projects

Learn More

Read the full tutorial: Building an MCP Server on Cloudflare Workers with Semantic Search

License

MIT

Author

Daniel Nwaneri - GitHub | Upwork