NebulaFinger MCP is an MCP server that connects large language model interfaces to the NebulaFinger reconnaissance and fingerprinting engine. Built with FastMCP, this server exposes local network and web asset identification capabilities to AI assistants such as Claude Desktop and Cherry Studio. Security researchers, penetration testers, and system administrators use it to identify web technologies, software frameworks, and running network services across target hosts without leaving their conversational interface. The integration exposes specialized commands to execute target assessments, process URL lists, parse structured files, and extract component signatures such as HTTP headers and favicons. By communicating over standard input and output (stdio), NebulaFinger MCP executes scanning routines through the underlying NebulaFinger binary and returns structured fingerprinting data back to the language model. Operators can inspect individual web endpoints, perform batch network scans with configurable concurrency, check database status, and export formatted HTML reports directly through natural language prompts. This setup automates initial asset reconnaissance workflows while keeping scanning configurations and target lists organized locally.
Category: Other & General Purpose
Tags: authentication, biometrics, fingerprint, identity, security
bash pip install fastmcp 2. Download or compile the NebulaFinger.exe binary (or Unix binary) and ensure your folder structure contains configs/ and MCP/mcp_server.py. 3. Add the server to your MCP client configuration file: json { "mcpServers": { "nebulafinger_mcp": { "isActive": true, "name": "nebulafinger_mcp", "type": "stdio", "command": "python", "args": [ "D:\\NebulaFinger\\MCP\\mcp_server.py" ], "env": { "NEBULAFINGER_PATH": "D:\\NebulaFinger" } } } } 4. Replace the path in args and NEBULAFINGER_PATH with your absolute installation path, then restart your client.Part of MCP Servers
NebulaFinger MCP allows AI models to run web and service fingerprint scans on target hosts. It provides tools to scan single domains, execute batch evaluations across target lists or external files, adjust concurrency levels, filter results based on fingerprint matches, and generate HTML reports directly from conversational prompts.
Installation requires Python 3.8 or higher, the FastMCP Python package, and the compiled NebulaFinger binary. After installing FastMCP with pip, place the mcp_server script in the MCP directory alongside your configs folder. Then configure your MCP client with the stdio command executing the Python script and setting the NEBULAFINGER_PATH environment variable.
NebulaFinger MCP uses the standard stdio Model Context Protocol transport. It works with any MCP-compatible environment that supports stdio process execution and custom environment variables, such as Claude Desktop and Cherry Studio, when paired with supporting language models.
Yes. Although the documentation frequently references NebulaFinger.exe on Windows, the core NebulaFinger tool can be built from source code on Linux and macOS systems. You can compile an executable binary without the exe extension and reference that executable path in your server configuration.
If the server reports missing configuration errors, ensure the configs directory exists at the root of your NebulaFinger directory. Verify that web and service fingerprint JSON files are present and encoded in UTF-8. Confirm that the NEBULAFINGER_PATH environment variable in your client configuration points to the exact absolute path.