Redmine MCP Server

The Yonaka15 MCP Server for Redmine bridges the gap between large language models (LLMs) and enterprise issue tracking by exposing a rich, type‑safe interface to Redmine’s REST API. Instead of writing custom integrations or parsing raw JSON, developers can hand the server to an LLM such as Claude and let it query, create, update, or delete issues, projects, users, time entries, and more—all through natural language prompts. This server solves the problem of manual data entry and repetitive API calls, enabling AI assistants to act as real‑time collaborators in project management workflows.

At its core, the server offers stable resources for Issues, Projects, Users, and Time Entries. Each resource is backed by a set of tools that map directly to common Redmine operations: searching with filters, creating new records with custom fields, updating existing entries, and deleting when necessary. The tools are designed to be intuitive for both developers and end users; for example, the Search Issues tool accepts parameters like project ID, status, or assignee and returns a concise list of matching tickets. This level of abstraction eliminates boilerplate code, reduces API‑level errors, and ensures consistent data formatting across all interactions.

Key capabilities include:

• Fine‑grained filtering: Search by project, status, assignee, date ranges, or custom fields.
• Full CRUD support: Create, update, and delete issues, projects, time entries, and users (the latter requiring admin rights).
• Custom field handling: Automatically maps Redmine’s flexible custom fields to structured inputs, preserving metadata.
• Administrative operations: Exposes privileged actions such as listing or modifying users, with clear permission checks.
• Robust validation: Uses the MCP SDK and schemas to validate request payloads, preventing malformed requests.

Real‑world scenarios where this server shines include:

• Agile teams: An AI assistant can pull sprint backlogs, suggest task assignments, or log time entries on the fly while developers are in a chat session.
• Project onboarding: New contributors can ask the assistant to create project modules, set up trackers, or archive old projects without touching the UI.
• Reporting: Generate time‑entry summaries or issue status reports automatically, feeding data into dashboards or email notifications.
• Automation pipelines: Trigger Redmine updates from CI/CD workflows or chatops commands, ensuring that build failures or deployment notes are logged as issues.

Integration into existing AI workflows is straightforward. A developer configures the server’s command, arguments, and environment variables (Redmine host and API key) in the client’s MCP configuration file. Once running, the server exposes its tools over the MCP protocol; an LLM can then invoke them using natural language, and the server translates those calls into authenticated REST requests. The result is a seamless, conversational bridge between human intent and project management data, enabling developers to focus on code while the AI handles ticketing and tracking tasks.