Trello MCP Server (TypeScript)

Overview

The Andypost MCP Server for Trello is a fully‑typed TypeScript implementation that exposes the entire Trello REST API as a set of Model Context Protocol (MCP) tools. It allows AI assistants—such as Claude or other MCP‑enabled agents—to query, manipulate, and retrieve information from Trello boards without writing custom integration code. By acting as a bridge between the AI’s natural language queries and Trello’s HTTP endpoints, this server turns conversational commands into concrete API calls that return structured JSON.

The primary problem it solves is the friction developers face when connecting AI assistants to third‑party productivity services. Without an MCP layer, a developer would need to manually craft HTTP requests, handle authentication, and parse responses for every new action. The Trello MCP server removes this boilerplate: developers simply define the desired tool (e.g., , ) and the AI can invoke it with a concise request payload. This accelerates prototype development, reduces bugs related to manual HTTP handling, and ensures consistent error reporting across all Trello operations.

Key features include:

• Full API coverage: All major Trello endpoints—boards, lists, cards, and card details—are available as discrete MCP tools.
• Asynchronous execution: Operations are non‑blocking, enabling the AI to continue processing while waiting for Trello’s responses.
• Type safety: The server is written in TypeScript, providing compile‑time guarantees for request and response shapes, which translates into reliable tool contracts for the AI.
• Robust error handling: Authentication failures, rate limits, network issues, and invalid parameters are captured and returned in a uniform error format, helping the AI gracefully inform users or retry.
• Environment‑driven configuration: API credentials are read from files, keeping secrets out of source code and simplifying deployment across environments.

In real‑world scenarios, teams can use this server to build AI‑powered project management assistants. For example, a developer might ask the assistant to “list all open cards in the ‘Sprint Backlog’ board” and receive a neatly formatted list without writing any code. Project managers can automate status updates, generate progress reports, or trigger webhook notifications by chaining MCP calls within the assistant’s workflow. Because each tool returns structured data, downstream processes—such as generating summaries or feeding information into other systems—can be composed declaratively.

Integration with AI workflows is straightforward: the server exposes a simple command‑line entry point that can be launched by any MCP client. In Cline, for instance, a single configuration entry registers the server and its tools; thereafter, the assistant can invoke , , , or directly. The server’s clear contract and consistent response format make it a drop‑in component for any AI pipeline that requires Trello data, providing developers with a powerful, low‑maintenance bridge between conversational agents and task management workflows.