Reptor MCP Server

Reptor mcp mcp server connects Reptor MCP to AI assistants that speak the Model Context Protocol. This project transforms the reptor CLI tool into an MCP (Model-Context-Protocol) server, exposing its powerful pentest reporting and automation features as a programmable service.

What Reptor MCP does

This project transforms the reptor CLI tool into an MCP (Model-Context-Protocol) server, exposing its powerful pentest reporting and automation features as a programmable service.

Key capabilities

• Dynamic Tool Generation: — Automatically creates MCP tools from all available reptor plugins (nmap, nessus, burp, zap, sslyze, etc.)
• Direct API Tools: — Provides structured tools for findings CRUD, schema discovery, and template management using reptor's Python API directly
• Field Exclusion: — Strips sensitive fields from data before returning it to LLM clients (configurable via environment variable)
• Async-Safe: — Non-blocking event loop with thread-safe serialized plugin execution

Tools it exposes

Once connected, the assistant can call these 10 tools directly:

• list_findings — Lists findings with filters (status, severity, title)
• get_finding_details — Gets full details of a finding by ID
• get_finding_schema — Discovers available finding fields, types, and constraints for a project. Call before create_finding or patch_finding
• create_finding — Creates a new finding from a flat data dict
• patch_finding — Updates a single field on a finding
• delete_finding — Deletes a finding by ID (requires explicit confirmation)
• upload_template — Uploads a finding template from JSON or TOML
• Category — Tools
• Templates — template
• Translation — translate (via DeepL)

Installing the reptor mcp mcp server

Setup follows the standard MCP pattern: clone or install the server, then register it in your client's configuration file and restart the client. The configuration snippets on this page cover Claude Desktop, Claude Code and Cursor.

Configuration

Before the server will start you need to supply 3 environment variables: REPTOR_MAIN_PATH, REPTOR_TOKEN, REPTOR_PROJECT_ID. Keep credentials in your client's env block or a secrets manager rather than committing them.

Requirements

• Python 3.10+ * uv (recommended) or pip * A running SysReptor instance with an API token

Where it fits

AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. Reptor MCP sits in that group, and the shape of its toolset — list_findings, get_finding_details, get_finding_schema among others — tells you what it is really for. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.

Practical notes

• This server runs locally, so it operates with whatever access your machine and its credentials already have. Scope that deliberately rather than by default.
• It will not start until its required credentials are present, so set those before wondering why the tools never appear.
• With 10 tools exposed, expect a noticeable bump in prompt size — disable it in projects that never use Reptor MCP.
• Maintained by slvnlrt, written in Python.
• MCP clients ask for confirmation before each tool call by default. Keep that on while you learn what the reptor mcp mcp server actually does with your data.
• Every entry in this directory is reviewed by hand before it goes live, and details are checked against the project's own documentation.