Sandbox
@baryhuang/mcp-hubspot

MCP server for HubSpot CRM data

HubSpot MCP Server exposes HubSpot contacts, companies, activity, and conversation data to AI assistants through MCP tools. It also stores embeddings with FAISS and caches them so the assistant can search earlier HubSpot context instead of starting over each time.

128 stars60 forksPythonUpdated 10mo ago
Who it's for

Builders who want their agent to work directly with HubSpot CRM data and remember past CRM context.

What it delivers

You can ask an agent to create, search, and review HubSpot records without switching out of your chat.

What it does

Contact and company tools

Creates contacts and companies with duplicate prevention.

Activity and conversation lookup

Fetches company activity, active contacts, active companies, and recent conversation threads.

Semantic search over prior data

Uses FAISS-backed vector storage to search previously retrieved HubSpot data by meaning.

Embedding caching and persistence

Caches embeddings with SentenceTransformer and keeps storage between sessions in a configurable directory.

Docker and Smithery setup

Ships with Docker images and Smithery install support for quick deployment.

How to get it

  1. 1Run
    # 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
  2. 2To build the Docker image locally
    git clone https://github.com/buryhuang/mcp-hubspot.git
    cd mcp-hubspot
    docker build -t mcp-hubspot .
  3. 3For multi-platform builds
    docker buildx create --use
    docker buildx build --platform linux/amd64,linux/arm64 -t buryhuang/mcp-hubspot:latest --push .

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:

ToolPurpose
hubspot_create_contactCreate contacts with duplicate prevention
hubspot_create_companyCreate companies with duplicate prevention
hubspot_get_company_activityRetrieve activity for specific companies
hubspot_get_active_companiesRetrieve most recently active companies
hubspot_get_active_contactsRetrieve most recently active contacts
hubspot_get_recent_conversationsRetrieve recent conversation threads with messages
hubspot_search_dataSemantic 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

Files in the repo

Repository payload11 top-level entries
  • src
  • tests
  • .gitignore
  • CLAUDE.md
  • Dockerfile
  • LICENSE
  • poetry.lock
  • pyproject.toml
  • README.md
  • smithery.yaml
  • uv.lock

Discussion (0)

Ask about usage, or say what you built with it

Sign in to join the discussion.

No comments yet. Be the first to say what this is good for.

More connectors

Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface

86k

High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

43k

Universal provider proxy for OpenAI Codex & Claude Code — use any LLM (Claude, Gemini, Grok, DeepSeek, Ollama…) with Codex CLI, App, SDK, and Claude Code

14k
okf-memory/
okf-agent-memory

Git-native persistent memory for AI coding agents. Implements Google OKF v0.2 with sub-300µs in-memory BM25 search, embedded MCP server, and progressive disclosure. Slashes token bloat by 80% with zero external databases or dependencies. Built in pure Go.

547
2akouwu/
reverify

Stop your AI from making things up — it proposes, deterministic tools decide, every claim checked against ground truth with evidence. Grounded facts and context survive resets. Reverse engineering is the proving ground. MCP server + CLI.

1.1k
t8y2/dbxConnectors

20 MB lightweight cross-platform database client for 90+ databases, including MySQL, PostgreSQL, SQLite, Redis, MongoDB, DuckDB, SQL Server, and Dameng. Built-in AI, MCP Server, CLI, desktop and Docker. | 轻量级跨平台数据库管理工具,支持 MySQL、PostgreSQL、SQLite、Redis、MongoDB、达梦等 90+ 数据库,提供桌面端、Docker、CLI、内置 AI 助手和 MCP Server。

19k