mcp-people-finder 0.1.1

Enterprise-grade People Finder MCP Server

MCP People Finder

Model Context Protocol (MCP) Server for AI agents to safely discover and query employee information.

This is a demo/educational project using simulated data to illustrate how MCP servers work. It demonstrates how to build enterprise-grade tools that AI agents can call securely over standard input/output (stdio).

What is MCP?

Model Context Protocol enables AI agents (like Claude) to call external tools in a standardized, type-safe way. Instead of embedding logic directly, agents can request specific information through well-defined tool interfaces. This server uses stdio for communication — a client (like Claude Desktop) starts your Python script as a background process.

Features

• Employee Search — Query employee details (role, email, status)
• Office Location Lookup — Find office addresses by city
• Global Office Directory — List all office locations
• Production-Ready Structure — Demonstrates enterprise MCP patterns
• Easy Testing — Built-in MCP Inspector integration

Prerequisites

• Python 3.11+ — Required for running the server
• Node.js — Required for MCP Inspector testing (optional, for development only)

Installation

pip install mcp-people-finder

Usage

Once installed, you can run the server directly:

people-finder

Expected Output

$ people-finder
MCP People Finder Server running on stdio...

Available Tools

Tool	Input	Output
search_employee	Employee name	Role, email, employment status
get_office_location	City/location	Office address, phone
list_all_offices	(none)	List of all global office locations

Testing & Development

Use MCP Inspector for local testing

For testing use the MCP Inspector (npx @modelcontextprotocol/inspector) to test this MCP server locally.

Run this command on your machine (it requires Node.js):

npx @modelcontextprotocol/inspector people-finder

• This will launch a web browser window.
• You will see your get_employee_info tool listed.
• You can click "Run", type "Alice" in the box, and see the result.
• Why this matters: This is how you "smoke test" your production server to ensure the logic works before letting an expensive AI Agent touch it.

Use with Claude Desktop

To integrate this MCP server with Claude Desktop:

1. Ensure the package is installed: pip install mcp-people-finder
2. Configure Claude Desktop config file to point to people-finder command
3. Restart Claude Desktop to load the server
4. Claude will now have access to all three tools

Error Handling

If a tool call fails, the server returns a graceful error response:

{
  "error": "Employee 'XYZ' not found in database"
}

This allows Claude to handle errors intelligently (retry, ask for clarification, etc.) rather than crashing.

Configuration

The server reads from environment variables (optional):

• MCP_LOGLEVEL — Set to DEBUG for verbose output (default: INFO)
• EMPLOYEE_DB — Path to custom employee database (currently uses hardcoded demo data)

Need Help?

• MCP Documentation: https://modelcontextprotocol.io/
• Claude API: https://claude.ai/
• Report Issues: Create a GitHub issue with detailed error messages