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Building an AI Agent Honeypot & Lead Engine with Model Context Protocol (MCP) & FastAPI
AI & Machine Learning

Building an AI Agent Honeypot & Lead Engine with Model Context Protocol (MCP) & FastAPI

Ali NematiAli NematiAug 31
1m & 2 s read7 views0 listens

Summary of the Project: MCP Collector

The project, named "MCP Collector," is designed to serve as a backend service for AI agents and models adhering to the Model Context Protocol (MCP). This protocol enables seamless integration with various AI platforms such as Claude Desktop, ChatGPT, Gemini, and custom-built agents. The goal of this project is to provide real-time visibility into how these AI tools interact with your services.

Key Features

  1. Model Context Protocol Implementation:

    • The backend service supports the Model Context Protocol (MCP), which standardizes interactions between AI models and external tools.
    • It includes a llms.txt file for machine-readable discovery, making it easy for autonomous web agents to find and use your APIs.

  2. Real-Time Telemetry:

    • Utilizes WebSockets to push events in real-time without the need for polling, ensuring instant updates when an agent invokes a tool.

  3. Server-Sent Events (SSE):

    • Provides persistent connections via SSE for event-driven notifications and data streaming.

  4. Google Cloud Run Deployment:

    • Deployed on Google Cloud Run to ensure scalability, with the ability to scale down to

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