Use This MCP server To

Enable AI assistants to query HubSpot CRM data Cache HubSpot data for faster AI response times Store vector embeddings of CRM data for semantic search Automate multi-step CRM workflows via AI Bridge AI models with HubSpot contacts and companies Improve API stability with built-in error handling Support AI-driven customer engagement analysis

README

HubSpot MCP Server

Docker Hub License: MIT

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

mcp-hubspot FAQ

How does mcp-hubspot improve HubSpot API limitations?

It uses built-in vector storage and caching to reduce API calls and speed up responses.

Can mcp-hubspot handle complex multi-step CRM workflows?

Yes, it is optimized for reliable performance during complex AI-driven CRM interactions.

Which AI assistants can connect to mcp-hubspot?

It supports Claude and other AI assistants compatible with the Model Context Protocol.

Does mcp-hubspot provide error handling for API requests?

Yes, it includes robust error handling to maintain API stability and reliability.

Is mcp-hubspot suitable for semantic search on CRM data?

Yes, its vector storage enables semantic search capabilities on HubSpot data.

How does mcp-hubspot enhance AI assistant productivity?

By providing direct, efficient access to HubSpot CRM data and automating workflows.

What data from HubSpot can mcp-hubspot access?

It accesses contacts, companies, and engagement data within HubSpot.

Is mcp-hubspot open source and where can I find it?

Yes, it is MIT licensed and available on GitHub and Docker Hub.