FindMyClient MCP Server

MCP server:

https://mcp.findmyclient.org/mcp

⚡ Why this exists

Manually hunting for verified business emails is a time sink. This server exposes FindMyClient's job-queue-based enrichment engine as native MCP tools, so any MCP-compatible client (Claude Desktop, Cursor, Windsurf, your own agent) can:

• Kick off async lead searches without babysitting a job queue

• Pull back MX-verified emails, not guesses

• Fold lead enrichment straight into an agentic outreach workflow

Related MCP server: websearch

🧰 Available MCP Tools

AI assistants read this section directly — keep it exact if you fork this.

Internally, search_and_wait composes three FindMyClient API calls — job submission, status polling, and result retrieval — so the model only has to make one tool call instead of managing a poll loop itself.

Get your token from your FindMyClient dashboard → API Tokens. Full reference: docs.findmyclient.org/api-token

🚀 Quick Start

Prerequisites

• An MCP-compatible client (Claude Desktop, Claude Code, Cursor, Windsurf, etc.)

• A FindMyClient API key — grab one here

📺 Setup in 30 seconds

1. Connect via hosted endpoint (recommended)

No install required — FindMyClient MCP is hosted. Just point your client at the URL:

https://mcp.findmyclient.org/mcp

Claude Desktop / Claude.ai

Settings → Connectors → Add custom connector → paste the URL above → authenticate with your API key.

Cursor / Windsurf

Settings → Features → MCP → + Add New MCP Server

• Name: findmyclient

• Type: http

• URL: https://mcp.findmyclient.org/mcp

2. Self-hosted / local (optional)

git clone https://github.com/Rottie420/findmyclient-mcp
cd findmyclient-mcp
pip install -r requirements.txt

claude_desktop_config.json:

{
  "mcpServers": {
    "findmyclient": {
      "command": "python",
      "args": ["-m", "src.server"],
      "env": {
        "FINDMYCLIENT_API_KEY": "your_api_key_here"
      }
    }
  }
}

🛠️ Development & Debugging

Run locally

python -m src.server

Inspect with the MCP Inspector

npx -y @modelcontextprotocol/inspector python -m src.server

Use the Inspector to fire search_leads → get_job_status → get_leads manually and confirm the job-queue lifecycle resolves before wiring it into a client.

🏗️ Architecture

graph LR
    Client[AI Client / Claude] <-->|MCP over HTTP| Server[FindMyClient MCP Server]
    Server <-->|REST| API[FindMyClient Flask API]
    API --> Queue[Async Job Queue]
    Queue --> MX[MX Verification]
    Queue --> DB[(Lead Store)]

• Tools — the four functions above, mapped 1:1 to FindMyClient's /search, /status, /leads, and /verify endpoints

• Job queue — searches run async server-side; poll get_job_status until complete before calling get_leads

• Verification layer — every returned lead is MX-checked before it reaches the model, so agents aren't emailing dead addresses

📄 License

MIT