Charles Proxy MCP Server for AI Agents

Charles Proxy MCP Server for AI Agents

This repository provides an MCP (Message Communication Protocol) server implementation for Charles Proxy, designed to facilitate seamless integration with AI agents. It enables AI developers to programmatically interact with and analyze network traffic captured by Charles Proxy. This tool is crucial for building AI agents that require real-time network data for decision-making, debugging, or performance analysis.

What it Does

The Charles Proxy MCP Server acts as a bridge between the Charles Proxy application and your AI agents. It exposes a structured interface, allowing your agents to subscribe to network traffic events, retrieve captured requests and responses, and potentially control aspects of the proxy itself. This eliminates the need for manual parsing of Charles Proxy logs or complex scraping, providing a direct and efficient data stream for AI consumption.

Key Features

• Live Capture Integration: Directly access network traffic as it's being captured by Charles Proxy.
• Structured Traffic Analysis: Receive network data in a well-defined, machine-readable format, simplifying parsing and analysis for AI models.
• Agent-Friendly Tool Contracts: Designed with AI agents in mind, offering clear interfaces for data retrieval and interaction.
• Open Source (GitHub): Full access to the source code for customization and understanding.

Who it's For

This tool is specifically for AI developers and researchers who are building or enhancing AI agents that need to understand and interact with network communication. This includes:

• Developers creating AI agents for network monitoring and security analysis.
• AI engineers building agents that require insights into application behavior through network traffic.
• Researchers developing AI models for network protocol analysis or performance optimization.
• Anyone looking to automate the process of extracting and analyzing network data for AI-driven applications.