Hartrace

by rafsanbasunia

174 downloads

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About

Hartrace is an MCP server for analyzing HTTP traffic captures (HAR files). Its distinguishing feature is value provenance tracing: given any token, cookie, ID, or payload field, it reconstructs where the value was produced (which response set it) and where it was consumed (which…

Details

Author: rafsanbasunia
Downloads: 174
Categories: Other

- Provenance tracing (trace_value, trace_header) — follows any value across responses → requests, reporting JSON paths for body fields.
- Search toolkit — full regex search across URLs, headers, and request/response bodies; header, URL, endpoint, and query-parameter extraction.
- Inspection — per-request/response retrieval with base64 + gzip/deflate decoding, nested-JSON unwrapping, and size caps.
- Lifecycle maps — cookie_map and token_map summarize how cookies and high-entropy secrets flow through a session.
- Diffing — compare two captures by (method, url, ordinal) so repeated calls to the same endpoint align.
- Safe by construction — pagination with server-clamped limits, secrets redacted in inspection output, no exceptions across the tool boundary.

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Hartrace

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

From the repository

Install with pipx install hartrace, uvx hartrace, or pip install hartrace. Add it to your MCP client’s configuration (e.g., claude_desktop_config.json for Claude Desktop, .cursor/mcp.json for Cursor, or .vscode/mcp.json for VS Code). Then interact naturally: “Load ~/captures/login.har and tell me where the CSRF token comes from.” Hartrace exposes 19 tools for loading, inspection, search, provenance, and comparison. Loaded captures are referred to by the name returned from load_har.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "hartrace": {
            "hartrace": {
                "command": "python",
                "args": [
                    "/absolute/path/to/hartrace/har_mcp.py"
                ]
            }
        }
    }
}

McpServers

{
    "hartrace": {
        "command": "python",
        "args": [
            "/absolute/path/to/hartrace/har_mcp.py"
        ]
    }
}

hartrace

An MCP server for analyzing HTTP traffic captures (HAR files) — built so an AI agent can answer questions about a capture without reading the raw JSON into its context window.

Its distinguishing feature is value provenance tracing: given any token, cookie, id, or payload field, hartrace reconstructs where the value was produced (which response set it) and where it was consumed (which later requests sent it), as a compact timeline. Every other tool — search, inspection, diffing — is built to return small, structured results with hard size caps, so analysis stays cheap regardless of how large the capture is.

load_har("session.har")
trace_value("session", "<csrf token>")
  → set_by:  response #4 body, JSON path data.csrf
  → used_in: request #9 header X-CSRF, request #9 body field token

---

Why this exists

HAR files are large, deeply nested, and repetitive. The two common ways an AI ends up analyzing them are both bad: writing throwaway extraction scripts every session, or pasting raw HAR JSON into the context window (slow, expensive, and it overflows on anything real). A 100-entry capture can be several megabytes; a single gzipped response can be hundreds of kilobytes.

hartrace moves the extraction and correlation to the server. Tools return only what was asked for, capped. The questions that normally require reading many entries by hand — where did this auth token come from? which request produced this cookie? where is this id reused? — are answered in one call.

---

Features

- Provenance tracing — trace_value follows any value across the capture (responses → requests), reporting JSON paths for body fields. Works on tokens, cookies, ids, headers, and payload fields alike, not just cookies.
- Search toolkit — full regex search across URLs, headers, and request/response bodies; header finder; URL/endpoint finder; query-parameter extraction.
- Inspection — per-request and per-response retrieval with base64 + gzip/deflate decoding, nested-JSON unwrapping, binary detection, and size caps.
- Lifecycle maps — cookie_map and token_map summarize how cookies and high-entropy secrets flow through a session.
- Diffing — compare two captures by (method, url, ordinal) so repeated calls to the same endpoint align.
- Loading — from a local path or an http(s) URL (with SSRF protection and a size cap).
- Safe by construction — every list/search tool paginates with server-clamped limits; secrets are redacted in inspection output; no tool raises to the transport (errors are returned as structured values).

---

Installation

Requires Python 3.10+.

```bash

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From the repository

Claude Desktop / Cursor

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hartrace

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