# Charles-mcp

> Charles-mcp — heizaheiza-charles-mcp. Use this tool when you need to analyze and debug network traffic in a structured and efficient manner. It solves problems related to understanding and optimizing communication between AI agents and external services, providing live capture and analysis of traffic data. With inputs from network traffic and outputs of actionable insights, use Charles-mcp in contexts where detailed traffic analysis is crucial for AI agent development and debugging.

Canonical page: https://skillsregistry.net/skills/heizaheiza-charles-mcp  
JSON: https://api.skillsregistry.net/v1/skills/heizaheiza-charles-mcp

## Description

Charles Proxy MCP server for AI agents with live capture, structured traffic analysis, and agent-friendly tool contracts

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/heizaheiza/Charles-mcp)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "heizaheiza-charles-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/heizaheiza-charles-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/heizaheiza-charles-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt