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A1official MCP Server

Provides Redmine analytics, music playback, and web automation through an MCP server with real-time data access.

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "a1official-mcp": {
      "url": "http://localhost:3001",
      "headers": {
        "REDMINE_URL": "https://your-redmine.com",
        "GROQ_API_KEY": "YOUR_GROQ_KEY",
        "REDMINE_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

You will run an MCP server that provides Redmine analytics, music playback, and web automation via a Python-based backend and a lightweight frontend. This server enables real-time analytics, project insights, and automation tasks through a unified command surface that you can access from an MCP client.

How to use

You connect to the MCP server to query sprint analytics, bug tracking metrics, team workload, and trends, or to trigger automated actions such as web automation tasks. Start the backend MCP server and then use your MCP client to send requests to the server URL. The server exposes practical analytics endpoints and tools to fetch live data from your Redmine instance and to drive automation tasks built into the system.

How to install

Prerequisites you need before starting are Python 3.12 or newer, Node.js 18 or newer, and access to a Redmine instance with API support.

Step by step commands to set up and run the server locally:

# 1. Clone the repository
git clone https://github.com/a1official/mcp.git
cd mcp

# 2. Set up Python environment
python -m venv .venv
.venv\Scripts\activate  # Windows
source .venv/bin/activate  # Linux/Mac

cd backend
pip install -r requirements.txt

# 3. Set up Frontend (optional for UI)
cd frontend
npm install

# 4. Configure environment (example)
cp .env.example .env
# Edit .env to provide:
# REDMINE_URL=https://your-redmine.com
# REDMINE_API_KEY=your_api_key
# GROQ_API_KEY=your_groq_key

Additional sections

Configuration and startup details help you run the MCP server smoothly and securely. You will connect to the server via HTTP for standard API access and use the local runtime to run the backend server. You can also manage credentials via an environment file that is not committed to source control.

To start the backend server, you will run the command that launches the FastAPI server provided in the project, and it will listen on a local port for MCP clients to connect.

Available tools

sprint_committed_stories

Returns total issues committed in the current sprint across all issue types (bugs, features, stories).

sprint_completion_status

Provides completion metrics for the current sprint, including progress and burndown indicators.

tasks_in_progress

Counts issues currently in progress within the sprint.

blocked_tasks

Counts tasks that are blocked and delaying progress.

backlog_size

Reports the total backlog size and its characteristics.

high_priority_open

Lists high-priority items currently open in the backlog.

monthly_activity

Shows created vs closed activity during the current month.

bug_metrics

Aggregates bug-related statistics including severity, counts, and ratios.

team_workload

Analyzes workload distribution across team members.

throughput_analysis

Calculates weekly throughput and velocity trends.