Trello MCP Server Integration

What does “Trello” MCP Server do?

The Trello MCP Server is a Model Context Protocol (MCP) server designed to integrate Trello with AI assistants such as Claude Desktop, GitHub Copilot Chat, and other MCP-compatible clients. It enables seamless interaction between AI models and Trello boards, allowing users to automate and enhance their project management workflows through natural language commands. The server supports listing all Trello boards, reading board contents (lists and cards), creating new cards, moving cards between lists, adding comments to cards, and archiving cards, all from within an AI assistant interface. By exposing Trello as an MCP resource, this server connects AI-driven tools to the rich ecosystem of Trello, making it easier for developers and teams to manage tasks, collaborate, and streamline productivity directly from their preferred AI environments.

List of Prompts

No information about prompt templates is provided in the repository.

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List of Resources

• Boards as MCP resources: Access Trello boards directly as MCP resources, allowing structured data retrieval and LLM context integration.
• Board contents (Lists and Cards): Read lists and cards from any of your Trello boards, exposing them as resources for downstream AI tasks.
• Comments: Retrieve comments associated with Trello cards for contextual awareness in AI workflows.

List of Tools

• List all boards: Enumerates all Trello boards associated with the account.
• Read board contents: Retrieves lists and cards from a specified Trello board.
• Create new cards: Adds new cards to a selected Trello list.
• Move cards: Moves cards between lists within a board.
• Add comments to cards: Appends comments to specific Trello cards.
• Archive cards: Archives cards that are no longer needed.

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Use Cases of this MCP Server

• Project/Task Management: Automate the creation, movement, and archiving of Trello cards using AI assistants to streamline team workflow and reduce manual updates.
• Meeting Summaries and Action Items: Turn meeting notes or discussions into actionable Trello cards, assign to team members, and track progress directly from conversations.
• AI-Driven Board Analysis: Use AI to summarize board status, identify bottlenecks, or recommend next actions based on board data.
• Collaboration Enhancement: Allow AI-powered assistants to add comments, update cards, and keep everyone in sync across multiple boards without leaving the chat interface.
• Contextual Task Suggestions: Enable LLMs to suggest new tasks or organize cards based on ongoing project data or communication patterns.

How to set it up

Windsurf

No explicit Windsurf instructions are provided in the repository.

Claude

1. Ensure Node.js 20+ is installed for a local setup, or Docker for a containerized deployment.
2. Obtain your Trello API Key and Token from https://trello.com/app-key .
3. Find your Claude Desktop config file:
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Linux: ~/.config/claude/claude_desktop_config.json
4. Add the following configuration:

{
  "mcp": {
    "servers": {
      "trello-mcp": {
        "command": "node",
        "args": ["absolute/path/to/the/project/dist/index.js"],
        "env": {
          "TRELLO_API_KEY": "your_api_key",
          "TRELLO_TOKEN": "your_token",
          "TRELLO_BASE_URL": "https://api.trello.com/1"
        }
      }
    }
  }
}

5. Save the file and restart Claude Desktop.

Docker option

{
  "mcp": {
    "servers": {
      "trello-mcp": {
        "command": "docker",
        "args": [
          "run",
          "--rm",
          "-i",
          "-e",
          "TRELLO_API_KEY=your_api_key",
          "-e",
          "TRELLO_TOKEN=your_token",
          "-e",
          "TRELLO_BASE_URL=https://api.trello.com/1",
          "trello-mcp-server"
        ]
      }
    }
  }
}

Cursor

No explicit Cursor instructions are provided in the repository.

Cline

No explicit Cline instructions are provided in the repository.

Securing API Keys

API credentials should be set using the env key in the MCP server configuration. Example:

"env": {
  "TRELLO_API_KEY": "your_api_key",
  "TRELLO_TOKEN": "your_token",
  "TRELLO_BASE_URL": "https://api.trello.com/1"
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "trello-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “trello-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above, the Trello MCP Server provides strong integration capabilities for Trello with AI assistants, but lacks documentation on prompts and sampling support. Its resource/tool support is well documented and the setup instructions are solid. Sampling and root support are not mentioned.

Our opinion

This MCP server is well-documented regarding setup, tools, and resource exposure, but limited in prompt and advanced MCP feature support. For most developers wanting Trello+AI integration, it’s effective and straightforward, but power users may want more advanced MCP features in the future.

MCP Score