remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Integrations

Provides integration with HubSpot CRM, enabling AI models to interact with HubSpot data including contacts, companies, and engagements. Offers tools for retrieving, creating, and managing HubSpot objects, as well as accessing recent engagement history.

HubSpot MCP Server

Overview

A Model Context Protocol (MCP) server that enables AI assistants to interact with HubSpot CRM data. This server bridges AI models with your HubSpot account, providing direct access to contacts, companies, and engagement data. Built-in vector storage and caching mechanisms help overcome HubSpot API limitations while improving response times.

Our implementation prioritizes the most frequently used, high-value HubSpot operations with robust error handling and API stability. Each component is optimized for AI-friendly interactions, ensuring reliable performance even during complex, multi-step CRM workflows.

Why MCP-HubSpot?

Direct CRM Access: Connect Claude and other AI assistants to your HubSpot data without intermediary steps
Context Retention: Vector storage with FAISS enables semantic search across previous interactions
Zero Configuration: Simple Docker deployment with minimal setup

Example Prompts

Create HubSpot contacts and companies from this LinkedIn profile:
[Paste LinkedIn profile text]

What's happening lately with my pipeline?

Available Tools

The server offers tools for HubSpot management and data retrieval:

Tool	Purpose
`hubspot_create_contact`	Create contacts with duplicate prevention
`hubspot_create_company`	Create companies with duplicate prevention
`hubspot_get_company_activity`	Retrieve activity for specific companies
`hubspot_get_active_companies`	Retrieve most recently active companies
`hubspot_get_active_contacts`	Retrieve most recently active contacts
`hubspot_get_recent_conversations`	Retrieve recent conversation threads with messages
`hubspot_search_data`	Semantic search across previously retrieved HubSpot data

Performance Features

Vector Storage: Utilizes FAISS for efficient semantic search and retrieval
Thread-Level Indexing: Stores each conversation thread individually for precise retrieval
Embedding Caching: Uses SentenceTransformer with automatic caching
Persistent Storage: Data persists between sessions in configurable storage directory
Multi-platform Support: Optimized Docker images for various architectures

Setup

Prerequisites

You'll need a HubSpot access token with these scopes:

crm.objects.contacts (read/write)
crm.objects.companies (read/write)
sales-email-read

Quick Start

# Install via Smithery (recommended)
npx -y @smithery/cli@latest install mcp-hubspot --client claude

# Or pull Docker image directly
docker run -e HUBSPOT_ACCESS_TOKEN=your_token buryhuang/mcp-hubspot:latest

Docker Configuration

For manual configuration in Claude desktop:

{
  "mcpServers": {
    "hubspot": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "HUBSPOT_ACCESS_TOKEN=your_token",
        "-v", "/path/to/storage:/storage",  # Optional persistent storage
        "buryhuang/mcp-hubspot:latest"
      ]
    }
  }
}

Building Docker Image

To build the Docker image locally:

git clone https://github.com/buryhuang/mcp-hubspot.git
cd mcp-hubspot
docker build -t mcp-hubspot .

For multi-platform builds:

docker buildx create --use
docker buildx build --platform linux/amd64,linux/arm64 -t buryhuang/mcp-hubspot:latest --push .

Development

pip install -e .

License

MIT License

You must be authenticated.

A

security – no known vulnerabilities

A

license - permissive license

A

quality - confirmed to work

Tools

Enables AI models to interact with HubSpot CRM data and operations through a standardized interface, supporting contact and company management.

Related Resources

Reddit Discussion about this server