iflow-mcp_jamiesonio_defectdojo-mcp

# DefectDojo MCP Server [](https://badge.fury.io/py/defectdojo) <!-- Add this badge if/when published to PyPI --> This project provides a [Model Context Protocol (MCP)](https://github.com/modelcontextprotocol/specification) server implementation for [DefectDojo](https://github.com/DefectDojo/django-DefectDojo), a popular open-source vulnerability management tool. It allows AI agents and other MCP clients to interact with the DefectDojo API programmatically. ## Features This MCP server exposes tools for managing key DefectDojo entities: * **Findings:** Fetch, search, create, update status, and add notes. * **Products:** List available products. * **Engagements:** List, retrieve details, create, update, and close engagements. ## Installation & Running There are a couple of ways to run this server: ### Using `uvx` (Recommended) `uvx` executes Python applications in temporary virtual environments, installing dependencies automatically. ```bash uvx defectdojo-mcp ``` ### Using `pip` You can install the package into your Python environment using `pip`. ```bash # Install directly from the cloned source code directory pip install . # Or, if the package is published on PyPI pip install defectdojo-mcp ``` Once installed via pip, run the server using: ```bash defectdojo-mcp ``` ## Configuration The server requires the following environment variables to connect to your DefectDojo instance: * `DEFECTDOJO_API_TOKEN` (**required**): Your DefectDojo API token for authentication. * `DEFECTDOJO_API_BASE` (**required**): The base URL of your DefectDojo instance (e.g., `https://your-defectdojo-instance.com`). You can configure these in your MCP client's settings file. Here's an example using the `uvx` command: ```json { "mcpServers": { "defectdojo": { "command": "uvx", "args": ["defectdojo-mcp"], "env": { "DEFECTDOJO_API_TOKEN": "YOUR_API_TOKEN_HERE", "DEFECTD…