Overview

This MCP (Model Context Protocol) server integrates Apollo.io with assistant tooling so AI agents and developer tools can enrich people and organization data, execute targeted searches, and surface job or contact information programmatically. It exposes Apollo.io functionality as a set of MCP tools — each tool encapsulates a specific enrichment or search capability and accepts structured parameters that an assistant or orchestrator can pass at runtime.

For developers building contextual assistants, CRMs, or automation pipelines, the server simplifies calling Apollo.io by translating high-level requests (e.g., “enrich this contact” or “find marketing managers in Ireland”) into the appropriate Apollo API calls and returning structured results suitable for further reasoning, display, or storage.

Features

People enrichment (contact-level profile augmentation)
Organization enrichment (company-level firmographics and insights)
People search with rich filters (titles, locations, technologies, seniority, etc.)
Organization search with filters for domain, location, size, and more
Job posting lookup for specific companies
Email discovery via Apollo person IDs
Company employees listing

Installation / Configuration

Install the package from npm:

npm install @agentx-ai/apollo-io-mcp-server

To test the server locally using the Model Context Protocol inspector:

# install this package globally (for local development)
npm install -g .

# run the MCP inspector against the server
npx @modelcontextprotocol/inspector apollo-io-mcp-server

Set your Apollo API key in the environment before running:

export APOLLO_IO_API_KEY="your_apollo_api_key_here"
# on Windows (PowerShell)
$env:APOLLO_IO_API_KEY="your_apollo_api_key_here"

The inspector will present the available MCP tools and allow you to invoke them interactively. In production, run the server as part of your MCP host or deploy to your preferred Node.js environment with the same environment variable set.

Repository and package:

GitHub: https://github.com/AgentX-ai/apollo-io-mcp-server
npm: https://www.npmjs.com/package/@agentx-ai/apollo-io-mcp-server

Available Tools

Below is a concise map of the primary MCP tools the server provides.

Tool name	Purpose	Top parameters
people_enrichment	Enrich a person record with Apollo data	first_name, last_name, email, domain, organization_name, linkedin_url
organization_enrichment	Get company firmographics & insights	domain (required)
people_search	Query people with advanced filters	person_titles, person_locations, person_seniorities, q_keywords, page, per_page
organization_search	Query companies with filters	q_organization_domains_list, organization_locations, organization_num_employees_ranges
job_postings	Fetch job postings for an organization	organization_id, page, per_page
email_discovery	Resolve emails by Apollo person ID	person_id
company_employees	List employees for an organization	organization_id, page, per_page

Each tool follows the MCP pattern: a name and a JSON-serializable parameters object. Responses are returned as structured JSON suitable for downstream processing by an assistant or service.

Examples

Example: enrich a contact (people_enrichment) via the inspector or any MCP host — pass the following parameters:

{
  "first_name": "Jane",
  "last_name": "Doe",
  "email": "[email protected]",
  "domain": "example.com",
  "linkedin_url": "https://www.linkedin.com/in/janedoe"
}

Example: find marketing managers in Ireland (people_search):

{
  "person_titles": ["marketing manager"],
  "person_locations": ["ireland"],
  "include_similar_titles": true,
  "page": 1,
  "per_page": 25
}

Example: get company enrichment (organization_enrichment):

{
  "domain": "apollo.io"
}

Run these payloads using the MCP inspector command shown above or by invoking the MCP server endpoint in your host environment.

Use Cases

CRM augmentation: Automatically enrich incoming leads with firmographics and contact details before routing to sales reps.
Targeted outreach lists: Use people_search filters to build lists of prospects by title, location, company size, and technology stack.
Contextual assistant responses: When an assistant receives a user query like “Who is the VP of Sales at Acme?” it can call people_search and organization_enrichment to provide an answer with provenance.
Recruiter sourcing: Locate active job postings and candidates with matching job titles and geographies.
Email verification and discovery: Resolve or validate contact emails using Apollo person IDs to improve deliverability in outreach workflows.

Tips and Best Practices

Rate limits: Respect Apollo.io API rate limits and implement retry/backoff in your MCP host where appropriate.
Parameter hygiene: For organization domains, omit protocol and “www.” (e.g., “apollo.io”).
Pagination: Use page and per_page parameters to iterate large result sets; per_page commonly caps at 100.
Minimal inputs: people_enrichment can accept partial identifiers (email or linkedin_url) — provide as much as you have for better accuracy.

For full parameter lists and latest updates, consult the project README and the GitHub repository: https://github.com/AgentX-ai/apollo-io-mcp-server.

Apollo.io MCP Server for People and Organization Enrichment