HubSpot MCP Server
Enables interaction with HubSpot CRM data, providing tools to create contacts and companies, retrieve company activity and active contacts/companies, access recent conversation threads, and semantically search previously retrieved HubSpot data.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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.
Related MCP server: HubSpot MCP Server
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 |
| Create contacts with duplicate prevention |
| Create companies with duplicate prevention |
| Retrieve activity for specific companies |
| Retrieve most recently active companies |
| Retrieve most recently active contacts |
| Retrieve recent conversation threads with messages |
| 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:latestDocker 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
Tool Schema Changelog
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This server cannot be deployed
Maintenance
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- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to interact with HubSpot CRM data, providing tools to create and retrieve contacts, companies, activities, and conversations, plus semantic search across previously retrieved data. Built-in vector storage and caching help overcome HubSpot API limitations and improve response times.-
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