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Hunter MCP Server

Hunter MCP Server

Local
github.com/help116114Security0Updated 79w ago
Repository

About Hunter MCP Server

Hunter MCP Server connects AI assistants to the Hunter (Qianxin) API for information about internet-connected devices and services. It supports varied search criteria, result sampling, resource access, and CSV export.

Key features

  1. 01Search by IP, domain, port, and other criteria
  2. 02Paginate results and select fields
  3. 03Limit result size to reduce token usage
  4. 04Access search results through resource URIs
  5. 05Export search results to CSV

Use cases

  1. 01Investigate internet-connected assets by IP or domain
  2. 02Filter for protocol, port, or certificate details
  3. 03Control the size of large query responses
  4. 04Save query results for later analysis

MCP support

The MCP server queries device and service data through the Hunter API and provides search and CSV export tools; search results are also available as resources.

#red-team#security-tools#mcp-server

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Hunter MCP Server

A Model Context Protocol (MCP) server that provides access to Hunter (Qianxin) API functionality, allowing AI assistants to query information about internet-connected devices and services.

Installation

  1. Clone the repository: 
    git clone https://github.com/help116114/hunter-mcp-server.git
cd hunter-mcp-server
  2. Install dependencies: 
    npm install
  3. Build the server: 
    npm run build
  4. Set up your Hunter API key and email: 
    export Hunter_API_KEY="your-api-key-here"
  5. Start the server: 
    npm start

Setup

To integrate this server with Claude or other MCP-compatible AI assistants, follow these steps:

  1. Add the server to your MCP settings:
{
  "mcpServers": {
    "hunter": {
      "command": "node",
      "args": ["/path/to/hunter-mcp-server/build/index.js"],
      "env": {
        "HUNTER_API_KEY": "your-api-key-here"
      }
    }
  }
}
  1. Restart Claude to load the new MCP server.

Features

  • Search Functionality: Query Hunter's database using various search criteria such as IP, domain, header, web info, ICP 备案信息，protocol/port, app components, cert, vul, AS, and tls - jarm.
  • Result Sampling: Limit the response data to reduce token usage, which is useful when dealing with large datasets. Resource Management**: Provide access to search results as resources with proper URIs.
  • CSV Export: Export search results to a CSV file for further analysis.

Tools

search_hunter
  • Description: Search Hunter's database for devices and services.
  • Parameters:
    • query (required): Hunter search query (e.g., 'ip="1.1.1.1"').
    • page (optional): Page number for results pagination (default: 1).
    • size (optional): Number of results per page (default: 10).
    • fields (optional): List of fields to include in the results (e.g., ['ip', 'port', 'title']).
export_to_csv
  • Description: Export Hunter search results to a CSV file.
  • Parameters:
    • query (required): Hunter search query (e.g., 'ip="1.1.1.1"').
    • outputPath (required): Path to the output CSV file.
    • page (optional): Page number for results pagination (default: 1).
    • size (optional): Number of results per page (default: 10).
    • fields (optional): List of fields to include in the results (e.g., ['ip', 'port', 'title']).

Requirements

  • Node.js: The server is built using Node.js. Make sure - you have Node.js (version 14.17 or higher) installed on your system.
  • Hunter API Key: You need a valid Hunter API key to access the Hunter API. You can obtain the API key from the Hunter platform.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.