apollo-io

This MCP server exposes the Apollo.io API functionalities as MCP tools. The available tools are: people_enrichment for enriching data about a single person, organization_enrichment for enriching data about a single organization, people_search to search for people matching given criteria, organization_search to search for organizations, and organization_job_postings to retrieve job postings for a specific organization. The server reads the Apollo.io API key from the APOLLO_IO_API_KEY environment variable (or a .env file at the project root), and uses the ApolloClient to interact with the Apollo.io API endpoints. To use the tools, run the MCP server via the provided uv command, then issue requests through your MCP client to invoke the respective tool with the appropriate data models defined under the apollo/ directory.

Prerequisites:

• Python 3.8+ and pip
• Access to install/execute via uv (from Ultralytics/MCP ecosystem) or ensure the uv tool is available in PATH

Installation steps:

1. Clone or download this repository: git clone https://github.com/edwardchoh-apollo-io-mcp-server.git cd edwardchoh-apollo-io-mcp-server
2. Create and activate a Python environment (optional but recommended): python3 -m venv venv source venv/bin/activate # on Unix/macOS .\venv\Scripts\activate # on Windows
3. Install dependencies (if a requirements file exists in the project, e.g., requirements.txt): pip install -r requirements.txt or install any specific dependencies as required by the project (e.g., httpx, pydantic, fastapi, etc.).
4. Set your Apollo API key (environment variable or .env file): export APOLLO_IO_API_KEY=your-api-key-here # Unix/macOS set APOLLO_IO_API_KEY=your-api-key-here # Windows
5. Run the MCP server using uv: uv run mcp run server.py
6. Optional: create a .env file in the project root and include: APOLLO_IO_API_KEY=your-api-key-here

Environment variables:

• APOLLO_IO_API_KEY: Apollo.io API key required for authentication. You can place it in a .env file or export it in your shell before starting the server.

Tips:

• The API models live under the apollo/ directory (e.g., apollo/people.py, apollo/organization.py, etc.). Use these data models to structure inputs for each MCP tool.
• If you encounter API rate limits or authentication issues, verify that the API key is valid and that it has the necessary permissions for the Apollo.io endpoints used.
• For testing, you can temporarily set APOLLO_IO_API_KEY in your shell and run a quick tool invocation to validate connectivity.
• If you need to run the server behind different environments, consider using a .env file and a dotenv loader within your Python project to automatically populate environment variables.