Explorium API MCP Server

The Explorium MCP Server is a Model Context Protocol server used to interact with the Explorium API. It enables AI assistants to access Explorium's business and prospect data lookup capabilities.

📋 Table of Contents

• Overview
• Installation
• Setup for Development
• Running Locally
• Usage with AI Assistants
  • Claude Desktop
  • Cursor
• Project Structure
• Development Workflow
• Continuous Integration
• Building and Publishing
• License

Overview

The Explorium MCP Server allows AI assistants to access the extensive business and prospects databases from Explorium. This enables AI tools to provide accurate, up-to-date information about companies, industries, and professionals directly in chat interfaces.

Installation

Install the Explorium MCP Server from PyPI:

pip install explorium-mcp-server

The package requires Python 3.10 or later.

Setup for Development

1. Clone the repository:

git clone https://github.com/explorium-ai/mcp-explorium.git
cd mcp-explorium

1. Set up the development environment using uv:

# Install uv if you don't have it
pip install uv

# Create and activate the virtual environment with all development dependencies
uv sync --group dev

1. Create a .env file in the root directory with your Explorium API key:

EXPLORIUM_API_KEY=your_api_key_here

To obtain an API key, follow the instructions in the Explorium API documentation.

Running Locally

mcp dev local_dev_server.py

Usage with AI Assistants

Usage with Claude Desktop

1. Follow the official Model Context Protocol guide to install Claude Desktop and set it up to use MCP servers.

2. Add this entry to your claude_desktop_config.json file:

{
  "mcpServers": {
    "Explorium": {
      "command": "<PATH_TO_UVX>",
      "args": [
        "explorium-mcp-server"
      ],
      "env": {
        "EXPLORIUM_API_KEY": "<YOUR_API_KEY>"
      }
    }
  }
}

For development, you can use this configuration instead:

{
  "mcpServers": {
    "Explorium": {
      "command": "<UV_INSTALL_PATH>",
      "args": [
        "run",
        "--directory",
        "<REPOSITORY_PATH>",
        "mcp",
        "run",
        "local_dev_server.py"
      ],
      "env": {
        "EXPLORIUM_API_KEY": "<YOUR_API_KEY>"
      }
    }
  }
}

Replace all placeholders with your actual paths and API key.

Usage with Cursor

Cursor has built-in support for MCP servers.

To configure it to use the Explorium MCP server:

1. Go to Cursor > Settings > Cursor Settings > MCP
2. Add an "Explorium" entry with this command:

For development, use:

uv run --directory <repo_path> mcp run local_dev_server.py

You may turn on "Yolo mode" in Cursor settings to use tools without confirming under Cursor > Settings > Cursor Settings > Features > Chat > Enable Yolo mode.

Project Structure

mcp-explorium/
├── .github/workflows/        # CI/CD configuration
│   └── ci.yml               # Main CI workflow
├── src/                      # Source code
│   └── explorium_mcp_server/
│       ├── __init__.py      # Package initialization
│       ├── __main__.py      # Entry point for direct execution
│       ├── models/          # Data models and schemas
│       └── tools/           # MCP tools implementation
├── tests/                    # Test suite
├── .env                      # Local environment variables (not in repo)
├── local_dev_server.py       # Development server script
├── Makefile                  # Development shortcuts
├── pyproject.toml           # Project metadata and dependencies
└── README.md                # Project documentation

Development Workflow

1. Set up the environment as described in Setup for Development
2. Make your changes to the codebase
3. Format your code:

make format

1. Run linting checks:

make lint

1. Run tests:

make test

Continuous Integration

The project uses GitHub Actions for CI/CD. The workflow defined in .github/workflows/ci.yml does the following:

1. Version Check: Ensures the version in pyproject.toml is incremented before merging to main
2. Linting: Runs code style and formatting checks using ruff
3. Testing: Runs the test suite with coverage reporting
4. Deployment: Tags the repo with the version from pyproject.toml when merged to main

Building and Publishing

Building the Package

To build the package for distribution:

1. Update the version in pyproject.toml (required for every new release)
2. Run the build command:

uv build

This creates a dist/ directory with the built package.

Publishing to PyPI

To publish the package to PyPI:

1. Ensure you have twine installed:

uv pip install twine

1. Upload the built package to PyPI:

twine upload dist/*

You'll need to provide your PyPI credentials or configure them in a .pypirc file.

Automatic Versioning and Tagging

When changes are merged to the main branch, the CI workflow automatically:

1. Tags the repository with the version from pyproject.toml
2. Pushes the tag to GitHub