hummingbot-mcp

hummingbot-mcp

Enables Claude and Gemini CLI to interact with Hummingbot for automated cryptocurrency trading across multiple exchanges.

Category
Visit Server

README

drasticstatic working copy — Used by the Fortuna trading system. This is an independent repo created from a local clone of hummingbot/mcp. Upstream is tracked as a remote for voluntary comparison — changes are reviewed before applying.

# Check for upstream updates (review before applying)
git fetch upstream && git log upstream/main --oneline

MCP Hummingbot Server

An MCP (Model Context Protocol) server that enables Claude and Gemini CLI to interact with Hummingbot for automated cryptocurrency trading across multiple exchanges.

Installation & Configuration

Option 1: Using uv (Recommended for Development)

  1. Install uv (if not already installed):

    curl -LsSf https://astral.sh/uv/install.sh | sh
    
  2. Clone and install dependencies:

    git clone https://github.com/hummingbot/mcp
    cd mcp
    uv sync
    
  3. Create a .env file:

    cp .env.example .env
    
  4. Edit the .env file with your Hummingbot API credentials:

    HUMMINGBOT_API_URL=http://localhost:8000
    HUMMINGBOT_USERNAME=admin
    HUMMINGBOT_PASSWORD=admin
    
  5. Configure in Claude Code or Gemini CLI:

    {
      "mcpServers": {
        "hummingbot-mcp": {
          "type": "stdio",
          "command": "uv",
          "args": [
            "--directory",
            "/path/to/mcp",
            "run",
            "main.py"
          ]
        }
      }
    }
    

    Note: Make sure to replace /path/to/mcp with the actual path to your MCP directory.

Option 2: Using Docker (Recommended for Production)

  1. Create a .env file:

    touch .env
    
  2. Edit the .env file with your Hummingbot API credentials:

    HUMMINGBOT_API_URL=http://localhost:8000
    HUMMINGBOT_USERNAME=admin
    HUMMINGBOT_PASSWORD=admin
    

    Important: When running the MCP server in Docker and connecting to a Hummingbot API on your host:

    • Linux: Use --network host (see below) to allow the container to access localhost:8000
    • Mac/Windows: Change HUMMINGBOT_API_URL to http://host.docker.internal:8000
  3. Pull the Docker image:

    docker pull hummingbot/hummingbot-mcp:latest
    
  4. Configure in Claude Code or Gemini CLI:

    For Linux (using --network host):

    {
      "mcpServers": {
        "hummingbot-mcp": {
          "type": "stdio",
          "command": "docker",
          "args": [
            "run",
            "--rm",
            "-i",
            "--network",
            "host",
            "--env-file",
            "/path/to/mcp/.env",
            "-v",
            "$HOME/.hummingbot_mcp:/root/.hummingbot_mcp",
            "hummingbot/hummingbot-mcp:latest"
          ]
        }
      }
    }
    

    For Mac/Windows:

    {
      "mcpServers": {
        "hummingbot-mcp": {
          "type": "stdio",
          "command": "docker",
          "args": [
            "run",
            "--rm",
            "-i",
            "--env-file",
            "/path/to/mcp/.env",
            "-v",
            "$HOME/.hummingbot_mcp:/root/.hummingbot_mcp",
            "hummingbot/hummingbot-mcp:latest"
          ]
        }
      }
    }
    

    (Remember to set HUMMINGBOT_API_URL=http://host.docker.internal:8000 in your .env file)

    Note: Make sure to replace /path/to/mcp with the actual path to your MCP directory.

Cloud Deployment with Docker Compose

For cloud deployment where both Hummingbot API and MCP server run on the same server:

  1. Create a .env file:

    touch .env
    
  2. Edit the .env file with your Hummingbot API credentials:

    HUMMINGBOT_API_URL=http://localhost:8000
    HUMMINGBOT_USERNAME=admin
    HUMMINGBOT_PASSWORD=admin
    
  3. Create a docker-compose.yml:

    services:
      hummingbot-api:
        container_name: hummingbot-api
        image: hummingbot/hummingbot-api:latest
        ports:
          - "8000:8000"
        volumes:
          - ./bots:/hummingbot-api/bots
          - /var/run/docker.sock:/var/run/docker.sock
        environment:
          - USERNAME=admin
          - PASSWORD=admin
          - BROKER_HOST=emqx
          - DATABASE_URL=postgresql+asyncpg://hbot:hummingbot-api@postgres:5432/hummingbot_api
        networks:
          - emqx-bridge
        depends_on:
          - postgres
    
      mcp-server:
        container_name: hummingbot-mcp
        image: hummingbot/hummingbot-mcp:latest
        stdin_open: true
        tty: true
        env_file:
          - .env
        environment:
          - HUMMINGBOT_API_URL=http://hummingbot-api:8000
        depends_on:
          - hummingbot-api
        networks:
          - emqx-bridge
    
      # Include other services from hummingbot-api docker-compose.yml as needed
      emqx:
        container_name: hummingbot-broker
        image: emqx:5
        restart: unless-stopped
        environment:
          - EMQX_NAME=emqx
          - EMQX_HOST=node1.emqx.local
          - EMQX_CLUSTER__DISCOVERY_STRATEGY=static
          - EMQX_CLUSTER__STATIC__SEEDS=[emqx@node1.emqx.local]
          - EMQX_LOADED_PLUGINS="emqx_recon,emqx_retainer,emqx_management,emqx_dashboard"
        volumes:
          - emqx-data:/opt/emqx/data
          - emqx-log:/opt/emqx/log
          - emqx-etc:/opt/emqx/etc
        ports:
          - "1883:1883"
          - "8883:8883"
          - "8083:8083"
          - "8084:8084"
          - "8081:8081"
          - "18083:18083"
          - "61613:61613"
        networks:
          emqx-bridge:
            aliases:
              - node1.emqx.local
        healthcheck:
          test: [ "CMD", "/opt/emqx/bin/emqx_ctl", "status" ]
          interval: 5s
          timeout: 25s
          retries: 5
    
      postgres:
        container_name: hummingbot-postgres
        image: postgres:15
        restart: unless-stopped
        environment:
          - POSTGRES_DB=hummingbot_api
          - POSTGRES_USER=hbot
          - POSTGRES_PASSWORD=hummingbot-api
        volumes:
          - postgres-data:/var/lib/postgresql/data
        ports:
          - "5432:5432"
        networks:
          - emqx-bridge
        healthcheck:
          test: ["CMD-SHELL", "pg_isready -U hbot -d hummingbot_api"]
          interval: 10s
          timeout: 5s
          retries: 5
    
    networks:
      emqx-bridge:
        driver: bridge
    
    volumes:
      emqx-data: { }
      emqx-log: { }
      emqx-etc: { }
      postgres-data: { }
    
  4. Deploy:

    docker compose up -d
    
  5. Configure in Claude Code or Gemini CLI to connect to existing container:

    {
      "mcpServers": {
        "hummingbot-mcp": {
          "type": "stdio",
          "command": "docker",
          "args": [
            "exec",
            "-i",
            "hummingbot-mcp",
            "uv",
            "run",
            "main.py"
          ]
        }
      }
    }
    

    Note: Replace hummingbot-mcp with your actual container name. You can find the container name by running:

    docker ps
    

Server Configuration

On first run, the server creates a default configuration from environment variables (or uses http://localhost:8000 with default credentials). Configuration is stored in ~/.hummingbot_mcp/server.yml.

Using the configure_server Tool

# Show the current server configuration
configure_server()

# Update the host and port
configure_server(host="192.168.1.100", port=8001)

# Update credentials
configure_server(username="admin", password="secure_password")

# Update everything at once
configure_server(
    name="production",
    host="prod-server",
    port=8000,
    username="admin",
    password="secure_password"
)

Only the provided parameters are changed; omitted ones keep their current values. The client automatically reconnects after any update.

Environment Variables

The following environment variables can be set in your .env file for the MCP server:

Variable Default Description
HUMMINGBOT_API_URL http://localhost:8000 Initial default API server URL (used only on first run)
HUMMINGBOT_USERNAME admin Initial username (used only on first run)
HUMMINGBOT_PASSWORD admin Initial password (used only on first run)
HUMMINGBOT_TIMEOUT 30.0 Connection timeout in seconds
HUMMINGBOT_MAX_RETRIES 3 Maximum number of retry attempts
HUMMINGBOT_RETRY_DELAY 2.0 Delay between retries in seconds
HUMMINGBOT_LOG_LEVEL INFO Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)

Note: After initial setup, use the configure_server tool to update the server connection. Environment variables are only used to create the initial default configuration.

Requirements

  • Python 3.11+
  • Running Hummingbot API server
  • Valid Hummingbot API credentials

Available Tools

The MCP server provides tools for:

Server Management

  • configure_server: View or update the active Hummingbot API server connection
    • No parameters: show current server config
    • Any parameters: update and reconnect
    • Configuration persists in ~/.hummingbot_mcp/server.yml

Trading & Account Management

  • Account management and connector setup
  • Portfolio balances and distribution
  • Order placement and management
  • Position management
  • Market data (prices, order books, candles)
  • Funding rates
  • Bot deployment and management
  • Controller configuration

Development

To run the server in development mode:

uv run main.py

To run tests:

uv run pytest

Troubleshooting

The MCP server now provides comprehensive error messages to help diagnose connection and authentication issues:

Connection Errors

If you see error messages like:

  • ❌ Cannot reach Hummingbot API at <url> - The API server is not running or not accessible
  • ❌ Authentication failed when connecting to Hummingbot API - Incorrect username or password
  • ❌ Failed to connect to Hummingbot API - Generic connection failure

The error messages will include:

  • The exact URL being used
  • Your configured username (password is masked)
  • Specific suggestions on how to fix the issue
  • References to tools like configure_server

Common Solutions

  1. API Not Running:

    • Ensure your Hummingbot API server is running
    • Verify the API is accessible at the configured URL
  2. Wrong Credentials:

    • Use configure_server tool to update server credentials
    • Or check your .env file configuration
  3. Wrong URL:

    • Use configure_server tool to update the server URL
    • For Docker on Mac/Windows, use host.docker.internal instead of localhost
  4. Docker Network Issues:

    • On Linux, use --network host in your Docker configuration
    • On Mac/Windows, use host.docker.internal:8000 as the API URL

Error Prevention

The MCP server will:

  • Not retry on authentication failures (401 errors) - it will immediately tell you the credentials are wrong
  • Retry on connection failures with helpful messages about what might be wrong
  • Provide context about whether you're running in Docker and suggest appropriate fixes
  • Guide you to the right tools (configure_server) to fix issues

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured