MCP DevTools

MCP DevTools is a modular ecosystem that empowers AI assistants to act as first‑class clients for popular project management services such as Jira and Linear. By exposing these tools through the Model Context Protocol, developers can give conversational agents the ability to query, modify, and orchestrate work items without leaving their IDE or chat interface. This solves the common pain point of “context switching” where a user must open separate dashboards or command‑line tools to manage tickets, leaving the AI’s workflow fragmented.

At its core, the server translates natural‑language commands into concrete API calls. For Jira, it supports a full suite of operations—retrieving tickets, executing JQL queries, creating issues, and reading detailed fields—while Linear offers analogous actions for issue tracking, team management, and search. The framework is deliberately extensible: each integration is packaged as an independent MCP server that can be dropped into any client supporting the protocol. This means new services can be added with minimal boilerplate, keeping the system future‑proof as tooling landscapes evolve.

Key capabilities include:

• Command‑driven interaction: Users can issue concise statements like or and receive immediate, structured responses.
• Rich data handling: The servers return JSON payloads that can be rendered as tables, lists, or custom UI components within the AI client.
• Secure credential management: Environment variables (e.g., , ) are read at runtime, ensuring tokens never leak into the codebase.
• Developer‑friendly setup: The README provides step‑by‑step configuration for Cursor IDE, but any MCP‑capable platform can integrate the same way.

Real‑world scenarios benefit from this integration: a product manager can ask the AI to pull all high‑priority bugs, automatically create new tickets from user feedback, or generate sprint reports—all while staying in the same chat window. A developer can fetch code‑related issues, trigger builds, and track progress without leaving their editor. Because the protocol is stateless and language‑agnostic, teams can adopt it across diverse tooling stacks—GitHub Projects, Azure Boards, or custom APIs—by simply writing a new MCP package.

In summary, MCP DevTools turns AI assistants into powerful, context‑aware agents that bridge the gap between conversational interfaces and enterprise tooling. Its modular design, robust feature set for Jira and Linear, and seamless integration path make it a compelling choice for developers looking to embed intelligent task management directly into their workflows.