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Apollo.io MCP Server

README.md
# Apollo.io MCP Server

[![TypeScript](https://img.shields.io/badge/TypeScript-4.9.5-blue.svg)](https://www.typescriptlang.org/)
[![Apollo.io API](https://img.shields.io/badge/Apollo.io%20API-v1-orange.svg)](https://docs.apollo.io/reference/introduction)
[![MCP SDK](https://img.shields.io/badge/MCP%20SDK-1.8.0-green.svg)](https://github.com/modelcontextprotocol/sdk)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A powerful Model Context Protocol (MCP) server implementation for seamless Apollo.io API integration, enabling AI assistants to interact with Apollo.io data.

<a href="https://glama.ai/mcp/servers/@lkm1developer/apollo-io-mcp-server">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/@lkm1developer/apollo-io-mcp-server/badge" alt="Apollo.io Server MCP server" />
</a>

## Overview

This MCP server provides a comprehensive set of tools for interacting with the Apollo.io API, allowing AI assistants to:

- Enrich data for people and organizations
- Search for people and organizations
- Find job postings for specific organizations
- Perform Apollo.io operations without leaving your AI assistant interface

## Why Use This MCP Server?

- **Seamless AI Integration**: Connect your AI assistants directly to Apollo.io data
- **Simplified API Operations**: Perform common Apollo.io tasks through natural language commands
- **Real-time Data Access**: Get up-to-date information from Apollo.io
- **Secure Authentication**: Uses Apollo.io's secure API token authentication
- **Extensible Design**: Easily add more Apollo.io API capabilities as needed

## Installation

```bash
# Clone the repository
git clone https://github.com/lkm1developer/apollo-io-mcp-server.git
cd apollo-io-mcp-server

# Install dependencies
npm install

# Build the project
npm run build
```

## Configuration

The server requires an Apollo.io API access token. You can obtain one by:

1. Going to your [Apollo.io Account](https://app.apollo.io/)
2. Navigating to Settings > API
3. Generating an API key

You can provide the token in two ways:

1. As an environment variable:
   ```
   APOLLO_IO_API_KEY=your-api-key
   ```

2. As a command-line argument:
   ```
   npm start -- --api-key=your-api-key
   ```

For development, create a `.env` file in the project root to store your environment variables:

```
APOLLO_IO_API_KEY=your-api-key
```

## Usage

### Starting the Server

```bash
# Start the server
npm start

# Or with a specific API key
npm start -- --api-key=your-api-key

# Run the SSE server with authentication
npx mcp-proxy-auth node dist/index.js
```

### Implementing Authentication in SSE Server

The SSE server uses the [mcp-proxy-auth](https://www.npmjs.com/package/mcp-proxy-auth) package for authentication. To implement authentication:

1. Install the package:
   ```bash
   npm install mcp-proxy-auth
   ```

2. Set the `AUTH_SERVER_URL` environment variable to point to your API key verification endpoint:
   ```bash
   export AUTH_SERVER_URL=https://your-auth-server.com/verify
   ```

3. Run the SSE server with authentication:
   ```bash
   npx mcp-proxy-auth node dist/index.js
   ```

4. The SSE URL will be available at:
   ```
   localhost:8080/sse?apiKey=apikey
   ```

   Replace `apikey` with your actual API key for authentication.

The `mcp-proxy-auth` package acts as a proxy that:
- Intercepts requests to your SSE server
- Verifies API keys against your authentication server
- Only allows authenticated requests to reach your SSE endpoint

### Integrating with AI Assistants

This MCP server is designed to work with AI assistants that support the Model Context Protocol. Once running, the server exposes a set of tools that can be used by compatible AI assistants to interact with Apollo.io data.

### Available Tools

The server exposes the following powerful Apollo.io integration tools:

1. **people_enrichment**
   - Use the People Enrichment endpoint to enrich data for 1 person
   - Parameters:
     - `first_name` (string, optional): Person's first name
     - `last_name` (string, optional): Person's last name
     - `email` (string, optional): Person's email address
     - `domain` (string, optional): Company domain
     - `organization_name` (string, optional): Organization name
   - Example:
     ```json
     {
       "first_name": "John",
       "last_name": "Doe",
       "email": "john.doe@example.com"
     }
     ```

2. **organization_enrichment**
   - Use the Organization Enrichment endpoint to enrich data for 1 company
   - Parameters:
     - `domain` (string, optional): Company domain
     - `name` (string, optional): Company name
   - Example:
     ```json
     {
       "domain": "apollo.io"
     }
     ```

3. **people_search**
   - Use the People Search endpoint to find people
   - Parameters:
     - `q_organization_domains_list` (array, optional): List of organization domains to search within
     - `person_titles` (array, optional): List of job titles to search for
     - `person_seniorities` (array, optional): List of seniority levels to search for
   - Example:
     ```json
     {
       "person_titles": ["Marketing Manager"],
       "person_seniorities": ["vp"],
       "q_organization_domains_list": ["apollo.io"]
     }
     ```

4. **organization_search**
   - Use the Organization Search endpoint to find organizations
   - Parameters:
     - `q_organization_domains_list` (array, optional): List of organization domains to search for
     - `organization_locations` (array, optional): List of organization locations to search for
   - Example:
     ```json
     {
       "organization_locations": ["Japan", "Ireland"]
     }
     ```

5. **organization_job_postings**
   - Use the Organization Job Postings endpoint to find job postings for a specific organization
   - Parameters:
     - `organization_id` (string, required): Apollo.io organization ID
   - Example:
     ```json
     {
       "organization_id": "5e60b6381c85b4008c83"
     }
     ```

## Extending the Server

The server is designed to be easily extensible. To add new Apollo.io API capabilities:

1. Add new methods to the `ApolloClient` class in `src/apollo-client.ts`
2. Register new tools in the `setupToolHandlers` method in `src/index.ts`
3. Rebuild the project with `npm run build`

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Keywords

Apollo.io, Model Context Protocol, MCP, AI Assistant, TypeScript, API Integration, Apollo.io API, People Enrichment, Organization Enrichment, People Search, Organization Search, Job Postings, AI Tools

TDQS

B3/5.0

Scored across 7 tools

Disambiguation4/5

Most tools have distinct purposes, but there is some potential overlap between organization_search and organization_enrichment, as both involve company data retrieval. However, their descriptions clarify that search is for finding organizations while enrichment is for augmenting data on a known company, which helps mitigate confusion. The other tools target clearly different resources like employees, emails, job postings, and people.

Naming Consistency4/5

The naming follows a consistent snake_case pattern throughout, which is good. However, there is a minor inconsistency: most tools use a verb_noun format (e.g., organization_search, people_enrichment), but employees_of_company uses a noun_of_noun structure, deviating slightly from the pattern. This does not severely impact readability but is a noticeable deviation.

Tool Count5/5

With 7 tools, the count is well-scoped for a sales intelligence or recruitment domain, covering key operations like searching and enriching data for both organizations and people. Each tool appears to earn its place without feeling excessive or sparse, aligning well with the server's apparent purpose of data enrichment and lookup.

Completeness4/5

The tool set provides solid coverage for core workflows in the domain, including search and enrichment for both organizations and people, plus specific functions like finding employees and emails. A minor gap is the lack of update or delete operations, but this is reasonable for a read-heavy enrichment server. The surface supports common agent tasks without obvious dead ends.

Maintenance

ActivityInactive
ResponsivenessNo issues