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Connect AI assistants to HubSpot CRM data and contacts

HubSpot MCP server gives Claude direct CRM access - creating contacts/companies and semantically searching activity via FAISS.

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Works with hubspotdockerfaissclaude
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Updated 10 months ago
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Source checked Sep 10, 2026
Version 1.0.0
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Why it matters

Enable AI assistants like Claude to directly access, search, and manage your HubSpot CRM data including contacts, companies, and conversations without manual data exports or intermediary steps.

Outcomes

What it gets done

01

Create and update HubSpot contacts and companies with automatic duplicate prevention

02

Retrieve recent activity, conversations, and pipeline updates from your CRM

03

Search semantically across previously retrieved HubSpot data using vector storage

04

Cache and persist CRM data between sessions for faster AI responses

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/mcp-hubspot | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

HubSpot

HubSpot MCP server connects AI assistants directly to HubSpot CRM data, with FAISS-backed semantic search and caching to create/lookup records and search past activity. Use it to create or search HubSpot contacts/companies and check recent activity from Claude. Requires a HubSpot access token scoped for contacts, companies, and sales email read.

What it does

This MCP server connects AI assistants directly to HubSpot CRM data - contacts, companies, and engagement/conversation data - with built-in vector storage and caching to work around HubSpot API limits and speed up repeat lookups. It's built for the highest-value CRM operations rather than full API coverage, with error handling aimed at staying reliable through multi-step workflows.

When to use - and when NOT to

Use it when you want Claude to create or look up HubSpot contacts and companies, check recent activity or conversations, or semantically search across data it has already retrieved - for example a prompt like "create HubSpot contacts and companies from this LinkedIn profile: [pasted profile text]", or simply asking "what's happening lately with my pipeline?" It requires a HubSpot access token scoped for crm.objects.contacts (read/write), crm.objects.companies (read/write), and sales-email-read granted on the token.

Capabilities

Seven tools: hubspot_create_contact and hubspot_create_company (both with duplicate prevention), hubspot_get_company_activity, hubspot_get_active_companies, hubspot_get_active_contacts (most recently active), hubspot_get_recent_conversations (recent conversation threads with messages), and hubspot_search_data (semantic search across previously retrieved HubSpot data).

Under the hood: FAISS vector storage for semantic search, thread-level indexing so each conversation is retrievable individually, SentenceTransformer embeddings with automatic caching, and persistent storage in a configurable directory so retrieved data survives between sessions - mount it as a volume and the vector index isn't rebuilt from scratch on every container restart.

How to install

npx -y @smithery/cli@latest install mcp-hubspot --client claude

Or run the Docker image directly with your token:

docker run -e HUBSPOT_ACCESS_TOKEN=your_token buryhuang/mcp-hubspot:latest

For Claude Desktop, add a docker run entry with HUBSPOT_ACCESS_TOKEN in -e and, optionally, a volume mount for persistent storage. Multi-platform images (linux/amd64, linux/arm64) are published to Docker Hub; building locally is git clone + docker build -t mcp-hubspot ., or the same multi-platform build with docker buildx build --platform linux/amd64,linux/arm64. A pip install -e . local development install is also available for working on the server itself.

Who it's for

Sales and RevOps teams who want Claude to create and look up HubSpot records, check recent activity, and search past CRM data conversationally, instead of switching to the HubSpot UI. The project pitches itself on three points: direct CRM access with no intermediary steps, context retention across sessions via the FAISS-backed semantic search, and a zero-configuration Docker deployment for getting started quickly. It's released under the MIT License and published under the mcp-hubspot package name.

Source 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 .

FAQ

Common questions

Discussion

Questions & comments · 0

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