MCP Nuclei Server
@crazyMarky
A Nuclei security scanning server based on MCP (Model Control Protocol), providing convenient vulnerability scanning services.一个基于 MCP (Model Control Protocol) 的 Nuclei 安全扫描服务器,提供便捷的漏洞扫描服务。
Overview
What is MCP Nuclei Server?
MCP Nuclei Server is a Nuclei security scanning service developed based on the MCP (Model Control Protocol). It allows large language models to execute Nuclei security scans, supporting various scanning options and JSON-formatted result output.
How to use MCP Nuclei Server?
Install Python 3.8+, ensure the Nuclei binary is installed and configured, clone the repository, install UV, create a virtual environment, and install the mcp package. Configure your MCP client (e.g., CLINE) with the appropriate JSON, pointing to the uv command and the main.py script, and set the NUCLEI_BIN_PATH environment variable. Invoke scanning by specifying a target and optional parameters like templates, severity, or tags.
Key features of MCP Nuclei Server
- Execute Nuclei security scans via MCP
- Configurable template and tag filtering
- Severity-based vulnerability filtering (critical, high, medium, low, info)
- JSON format output for results
- Easy-to-integrate MCP service
Use cases of MCP Nuclei Server
- Automate security scanning of web targets via an LLM agent
- Integrate vulnerability detection into AI-powered workflows
- Run targeted scans with specific templates or severity filters
- Obtain structured JSON results for further analysis
FAQ from MCP Nuclei Server
What prerequisites are needed?
Python 3.8 or higher, and the Nuclei binary must be installed and configured on the system.
How do I configure the server?
Add an MCP server entry in your client’s JSON config with the uv command, the directory containing main.py, and the NUCLEI_BIN_PATH environment variable set to the Nuclei binary location.
What output format does it return?
Scan results are returned in JSON format, including fields for success status, target, time cost, and an array of findings with template name, severity, matched location, and vulnerability info.
How can I contribute?
Fork the repository, create a feature branch, commit your changes, push the branch, and open a Pull Request.