Hunter MCP Server Integration

What does “Hunter” MCP Server do?

The Hunter MCP Server provides integration between the Hunter API and any LLM provider supporting the Model Context Protocol (MCP), such as Claude for Desktop. This server enables AI assistants to access, query, and manipulate B2B data about people and companies from the Hunter platform using natural language. By exposing Hunter’s powerful API endpoints as tools, the server streamlines workflows like searching for domain information, finding and verifying email addresses, enriching company data, and managing leads. This tight integration empowers developers and business professionals to automate lead generation and enrichment tasks, allowing AI agents to interact seamlessly with external B2B datasets, thus enhancing productivity and accelerating business development processes.

List of Prompts

No explicit prompt templates are mentioned in the repository or documentation.

List of Resources

No specific MCP resources are listed in the repository or documentation.

List of Tools

• Domain Search
  Enables searching for email addresses associated with a specific domain.
• Email Finder
  Finds the most likely email address for a given person and domain.
• Email Verifier
  Verifies the validity and deliverability of an email address.
• Email Enrichment
  Retrieves additional data about a given email address.
• Company Enrichment
  Provides detailed information about a specific company.
• Lead Creation
  Allows saving contacts and companies as leads in Hunter.

Use Cases of this MCP Server

• Domain Research and Lead Generation
  Automate the discovery of company contacts and emails for sales and outreach campaigns by searching domains and identifying key personnel.
• Email Verification for Campaigns
  Validate email addresses before launching marketing or sales outreach to reduce bounce rates and improve deliverability.
• Contact and Company Enrichment
  Gather comprehensive details about companies and individuals to build richer customer profiles for CRM systems.
• Automated Lead Management
  Seamlessly add new leads to the Hunter platform through conversational AI interfaces, streamlining sales workflows.
• B2B Data Integration
  Integrate up-to-date B2B data into custom applications or AI agents to power real-time decision-making and personalization.

How to set it up

Windsurf

1. Ensure you have Python 3.13+ and uv installed.
2. Obtain a Hunter API key.
3. Edit or create your configuration file for Windsurf.
4. Add the Hunter MCP Server using the following JSON snippet:

   {
     "mcpServers": {
       "hunter-mcp": {
         "command": "uv",
         "args": [
           "run",
           "--with",
           "mcp[cli]",
           "mcp",
           "run",
           "PATH_TO_hunter-mcp/main.py"
         ],
         "env": {
           "HUNTER_API_KEY": "YOUR_HUNTER_API_KEY"
         }
       }
     }
   }
   

5. Save the configuration and restart Windsurf.
6. Verify the server is running and accessible.

Claude

1. Ensure Python 3.13+ and uv are installed.
2. Obtain your Hunter API key.
3. Run the following command in your terminal:

   mcp install main.py -v HUNTER_API_KEY=YOUR_HUNTER_API_KEY
   

4. Configure Claude to connect to the new MCP server.
5. Test access by invoking a Hunter tool via Claude.

Cursor

1. Install Python 3.13+ and uv.
2. Get your Hunter API key.
3. Locate the MCP configuration in Cursor settings.
4. Add the Hunter MCP Server as shown:

   {
     "mcpServers": {
       "hunter-mcp": {
         "command": "uv",
         "args": [
           "run",
           "--with",
           "mcp[cli]",
           "mcp",
           "run",
           "PATH_TO_hunter-mcp/main.py"
         ],
         "env": {
           "HUNTER_API_KEY": "YOUR_HUNTER_API_KEY"
         }
       }
     }
   }
   

5. Save and restart Cursor to enable the server.

Cline

1. Make sure Python 3.13+ and uv are installed.
2. Obtain your Hunter API key.
3. Edit the MCP configuration file for Cline.
4. Insert the following snippet:

   {
     "mcpServers": {
       "hunter-mcp": {
         "command": "uv",
         "args": [
           "run",
           "--with",
           "mcp[cli]",
           "mcp",
           "run",
           "PATH_TO_hunter-mcp/main.py"
         ],
         "env": {
           "HUNTER_API_KEY": "YOUR_HUNTER_API_KEY"
         }
       }
     }
   }
   

5. Save changes and restart Cline.

Securing API Keys

Always store sensitive API keys using environment variables, not hard-coded in configuration files. Example:

{
  "mcpServers": {
    "hunter-mcp": {
      "env": {
        "HUNTER_API_KEY": "${HUNTER_API_KEY}"
      },
      "inputs": {
        "HUNTER_API_KEY": "your-api-key"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "hunter-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “hunter-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server offers solid integration with the Hunter API, making B2B data and lead management accessible to LLM-powered tools. However, it lacks documented prompt templates and resource primitives, and does not mention advanced MCP features like roots or sampling. The code is open source with a permissive license, but the project is still early with modest community engagement. Overall, it’s a good choice for Hunter API users seeking MCP compatibility, but could benefit from more documentation and MCP feature support.

MCP Score