Backlog MCP Server

Overview

The Mcp Server Backlog provides a lightweight MCP (Model Context Protocol) server that bridges AI assistants, such as Claude, with the Backlog API. By exposing Backlog’s REST endpoints through MCP, developers can let AI agents query project data, create issues, or modify existing items without leaving the assistant’s environment. This solves the common pain point of manually switching between a web UI and code when working with Backlog, enabling seamless data retrieval and manipulation directly from conversational prompts.

At its core, the server implements a permission‑controlled gateway. Clients can request READ access to retrieve information via GET requests or opt for MUTATE privileges that allow full CRUD operations. The permission flag is passed as a command‑line argument, ensuring the assistant only performs actions that have been explicitly authorized. This design promotes security and aligns with best practices for integrating external services into AI workflows.

Key features include:

• Dynamic API key handling: The server reads the Backlog API key from a local file, simplifying credential management for developers.
• URL configuration: Future enhancements will let users pass the Backlog space ID and base URL via CLI, giving flexibility to target multiple Backlog workspaces.
• Modular MCP integration: The server can be registered with Claude using a single command, automatically exposing Backlog resources as MCP tools.
• Permission‑aware routing: The server validates each request against the granted permission level, preventing accidental data modifications.

Typical use cases involve:

• Issue triage: An AI assistant can list open tickets, provide status summaries, or flag overdue items during a stand‑up meeting.
• Automated reporting: Generate weekly progress reports by querying Backlog metrics and formatting them in natural language.
• Rapid prototyping: Developers can test Backlog integrations within the assistant’s sandbox before committing code to production.

By integrating this MCP server into an AI workflow, teams gain a unified interface for project management tasks. The assistant can act as a conversational front‑end to Backlog, reducing context switching and accelerating decision making. Its permission model ensures that sensitive operations remain controlled, while its straightforward setup encourages rapid adoption in both development and production environments.