Teamwork MCP Server

Overview

The Teamwork MCP Server provides a ready‑made bridge between Large Language Models (LLMs) and the Teamwork.com project management platform. By exposing Teamwork’s API operations as MCP tools, it allows AI assistants—whether Claude Desktop, VS Code Copilot Chat, Gemini, or any MCP‑compatible client—to perform real‑world project tasks directly from natural language prompts. Developers can therefore enrich their AI workflows with authenticated, typed interactions that respect Teamwork’s data model and permissions.

This server solves a common pain point: the need to write custom adapters for each LLM or toolchain. Instead of building bespoke HTTP wrappers, authentication flows, and data transformers, the MCP server declares a single schema that lists all available actions (e.g., creating tasks, updating timers, querying project tags). The LLM receives this schema and can invoke the appropriate tool with confidence that arguments are validated, responses are structured, and errors are surfaced uniformly. This standardization reduces integration friction and accelerates time‑to‑value for teams that already use Teamwork as their backbone.

Key capabilities include:

• Dual transport modes: An HTTP server for cloud or multi‑client deployments and a lightweight STDIO server for desktop or local development.
• Secure authentication: Supports both bearer tokens and OAuth2, allowing fine‑grained access control that mirrors Teamwork’s own security model.
• Extensible tool framework: New tools can be added by registering them in the internal package, enabling rapid feature expansion without touching protocol logic.
• Observability: Built‑in logging, metrics, and a read‑only mode for safety‑critical environments.

Typical use cases involve AI agents that automatically triage support tickets, generate sprint backlogs, or log time entries based on email content. For example, a developer could prompt the assistant to “create a new task for bug #1234 and assign it to Alice,” and the MCP server would translate that into a Teamwork API call, returning a structured response that the LLM can embed in its reply. In education or consulting settings, the server can expose a sandboxed read‑only view for auditing or reporting purposes.

Integration into existing AI workflows is straightforward: an MCP client (e.g., Claude’s prompt engine) fetches the tool manifest from the server, then calls tools via the MCP “tool invocation” schema. Because the server is protocol‑agnostic, it works seamlessly with any LLM that supports MCP, from OpenAI’s GPT series to Anthropic’s Claude. The result is a cohesive, secure, and developer‑friendly pipeline that turns Teamwork.com into an intelligent knowledge base for AI assistants.