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Showing content from https://github.com/buryhuang/mcp-hubspot below:

peakmojo/mcp-hubspot: A Model Context Protocol (MCP) server that enables AI assistants to interact with HubSpot CRM data, providing built-in vector storage and caching mechanisms help overcome HubSpot API limitations while improving response times.

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.

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

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

What's happening lately with my pipeline?

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

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

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

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

# 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

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"
      ]
    }
  }
}

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 .

MIT License

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