Backlog MCP Server

The Backlog MCP Server bridges the gap between Claude‑style AI assistants and the rich feature set of Backlog, a popular project management platform. By exposing Backlog’s REST API through the Model Context Protocol, developers can let their AI agents query projects, create and update issues, manage wikis, and more—all without writing custom API wrappers. This server turns Backlog into a first‑class tool in an AI workflow, enabling assistants to understand project status, pull up documentation, or even automate issue triage directly from natural language prompts.

At its core, the server provides a collection of high‑level tools that mirror Backlog’s own endpoints. Projects can be listed or fetched by ID, while issues support full CRUD operations with pagination and filtering. Wiki pages are likewise accessible, allowing AI assistants to read or modify documentation on demand. Each tool is accompanied by a clear description and optional parameters, so the AI can construct precise calls that match Backlog’s expectations. Because the server adheres to MCP standards, any Claude‑compatible client can discover these capabilities automatically and offer them as part of its conversational toolkit.

The value for developers lies in the abstraction layer it creates. Instead of handling OAuth tokens, rate limits, and JSON schemas manually, the MCP server handles authentication via environment variables and translates user intent into well‑formed API requests. This reduces boilerplate code, speeds up prototyping, and ensures consistent error handling across all tools. For teams that rely on Backlog for issue tracking and documentation, the server empowers AI assistants to become real collaborators—suggesting fixes, flagging overdue tasks, or pulling in relevant wiki pages without leaving the chat interface.

Typical use cases include:

• Automated issue triage: An AI assistant can scan new issues, assign priorities, and add comments based on project guidelines.
• Documentation retrieval: When a developer asks for the API contract, the assistant fetches the relevant wiki page and presents it inline.
• Project health reports: By aggregating project data, the assistant can generate status dashboards or alert on stalled milestones.
• Rapid prototyping: New features can be sketched in a conversation, with the assistant creating placeholder issues or wiki pages that developers can later flesh out.

Integration is straightforward. Once the MCP server is running—either via or Docker—the AI client adds it to its configuration. The client then automatically lists all available tools, allowing users to invoke them by name or through natural language. Because the server follows MCP conventions, it also supports context propagation and streaming responses, giving developers fine‑grained control over how information is returned.

Unique advantages of this implementation include its modular design for easy extension: adding a new Backlog endpoint only requires defining a schema, registering the tool, and implementing a handler. The server also respects Backlog’s rate‑limiting policies by exposing pagination parameters, ensuring that large queries can be performed safely. Finally, the open‑source MIT license encourages community contributions and rapid iteration, making it a sustainable choice for teams that want to keep their AI tooling tightly coupled with Backlog’s evolving API.