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Apollo.io MCP Server

Apollo.io MCP Server

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github.com/edwardchohBusiness & Marketing16Updated 81w ago
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About Apollo.io MCP Server

Apollo.io MCP Server exposes selected Apollo.io API operations as tools for MCP clients. It supports people and organization enrichment and search, as well as organization job-posting lookup, using an Apollo.io API key.

Key features

  1. 01Enrich person data
  2. 02Enrich organization data
  3. 03Search people
  4. 04Search organizations
  5. 05Find organization job postings

Use cases

  1. 01Enrich a person's data
  2. 02Search for people or organizations
  3. 03Look up a company's job postings

MCP support

The server exposes five Apollo.io API operations as MCP tools: people and organization enrichment, people and organization search, and organization job-posting lookup.

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

Apollo.io MCP Server

This project provides an MCP server that exposes the Apollo.io API functionalities as tools. It allows you to interact with the Apollo.io API using the Model Context Protocol (MCP).

Overview

The project consists of the following main components:

  • apollo_client.py: Defines the ApolloClient class, which is used to interact with the Apollo.io API. It includes methods for people enrichment, organization enrichment, people search, organization search, and organization job postings.
  • server.py: Defines the FastMCP server, which exposes the Apollo.io API functionalities as tools. It uses the ApolloClient class defined in apollo_client.py to interact with the API.
  • apollo/: Contains the data models for the Apollo.io API, such as PeopleEnrichmentQuery, OrganizationEnrichmentQuery, PeopleSearchQuery, OrganizationSearchQuery, and OrganizationJobPostingsQuery.

Functionalities

The following functionalities are exposed as MCP tools:

  • people_enrichment: Use the People Enrichment endpoint to enrich data for 1 person.
  • organization_enrichment: Use the Organization Enrichment endpoint to enrich data for 1 company.
  • people_search: Use the People Search endpoint to find people.
  • organization_search: Use the Organization Search endpoint to find organizations.
  • organization_job_postings: Use the Organization Job Postings endpoint to find job postings for a specific organization.

Usage

To use this MCP server, you need to:

  1. Set the APOLLO_IO_API_KEY environment variable with your Apollo.io API key. Or create '.env' file in the project root with APOLLO_IO_API_KEY.
  2. Get dependencies: uv sync
  3. Run the uv run mcp run server.py

Data Models

The apollo/ directory contains the data models for the Apollo.io API. These models are used to define the input and output of the MCP tools.

  • apollo/people.py: Defines the data models for the People Enrichment endpoint.
  • apollo/organization.py: Defines the data models for the Organization Enrichment endpoint.
  • apollo/people_search.py: Defines the data models for the People Search endpoint.
  • apollo/organization_search.py: Defines the data models for the Organization Search endpoint.
  • apollo/organization_job_postings.py: Defines the data models for the Organization Job Postings endpoint.

Testing

To test, set APOLLO_IO_API_KEY environment variable and run uv run apollo_client.py.

Usage with Claude for Desktop

  1. Configure Claude for Desktop to use these MCP servers by adding them to your claude_desktop_config.json file:
{
  "mcpServers": {
    "apollo-io-mcp-server": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "run",
        "mcp",
        "run",
        "path/to/apollo-io-mcp-server/server.py"
      ]
    }
  }
}

Resources

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.