Redmine MCP Server

Redmine MCP Server

A Model Context Protocol (MCP) server that enables AI assistants to interact with Redmine for focused and transparent project management.

Overview

This MCP server provides a bridge between AI assistants and Redmine, allowing the AI to:

• Create and update issues with proper categorization
• Manage wiki pages and documentation
• Track project status and progress
• Follow defined processes for consistency

By using this MCP server, you can ensure that AI work remains focused, well-documented, and fully transparent to human team members.

Features

Resource Capabilities

• Issues: Access to Redmine issues with filtering and search
• Projects: Access to project data, categories, and statuses
• Wiki: Access to wiki pages for documentation

Tool Capabilities

• create_issue: Create new issues with proper categorization
• update_issue: Update existing issues with status changes and notes
• create_wiki: Create or update wiki pages for documentation
• get_project_status: Get project status summaries and statistics

Prompt Capabilities

• issue_template: Template for creating well-structured issues
• wiki_template: Template for creating well-structured wiki pages

Requirements

• Python 3.9+
• Flask
• Redmine instance with API access
• Claude Desktop or other MCP-compatible AI assistant

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/redmine-mcp-server.git
   cd redmine-mcp-server
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Configure the server:

   cp config.yaml.example config.yaml
   # Edit config.yaml with your Redmine URL and API key
   

Usage

Running the server

Start the server with:

python main.py

The server runs on port 5000 by default.

Docker deployment

Build and run the Docker container:

docker build -t redmine-mcp-server .
docker run -d -p 5000:5000 -e REDMINE_API_KEY=your_api_key -e REDMINE_URL=http://localhost:3000 redmine-mcp-server

Configuring Claude Desktop

Add the following to your Claude Desktop MCP configuration:

{
  "mcps": {
    "redmine": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "REDMINE_API_KEY",
        "-e",
        "REDMINE_URL",
        "redmine-mcp-server:latest"
      ],
      "environment": {
        "REDMINE_API_KEY": "your_redmine_api_key",
        "REDMINE_URL": "http://localhost:3000"
      }
    }
  }
}

API Reference

MCP Endpoints

• GET /mcp: Returns MCP capabilities
• GET /mcp/health: Returns health status

Resource Endpoints

• GET /mcp/resources/issues: Returns issues as resources
• GET /mcp/resources/projects: Returns project data
• GET /mcp/resources/wiki: Returns wiki pages

Tool Endpoints

• POST /mcp/tools/create_issue: Creates a new issue
• POST /mcp/tools/update_issue: Updates an existing issue
• POST /mcp/tools/create_wiki: Creates or updates a wiki page
• POST /mcp/tools/get_project_status: Gets project status and statistics

Prompt Endpoints

• GET /mcp/prompts/issue_template: Returns template for creating issues
• GET /mcp/prompts/wiki_template: Returns template for creating wiki pages

Configuration Options

The server can be configured using a config.yaml file or environment variables:

| Option | Environment Variable | Description | Default | |--------|----------------------|-------------|---------| | redmine_url | REDMINE_URL | URL of the Redmine instance | http://localhost:3000 | | redmine_api_key | REDMINE_API_KEY | API key for Redmine authentication | None | | server_port | SERVER_PORT | Port for the MCP server | 5000 | | log_level | LOG_LEVEL | Logging level (INFO, DEBUG, etc.) | INFO | | project_id | PROJECT_ID | Default Redmine project ID | 1 | | default_category_id | DEFAULT_CATEGORY_ID | Default category ID for issues | 3 | | default_tracker_id | DEFAULT_TRACKER_ID | Default tracker ID for issues | 2 |

Process Benefits

Using this MCP server provides several benefits for AI-assisted project management:

1. Structured Documentation: All AI work is automatically documented in Redmine
2. Clear Processes: AI tasks follow predefined workflows and categories
3. Transparency: All AI actions are logged and traceable
4. Collaboration: Human team members can easily review and contribute to AI work
5. Progress Tracking: Project managers can track AI task progress through Redmine

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.