mantis

Mantis MCP Server provides a set of MCP endpoints to interact with a MantisBT instance. It exposes tools for retrieving and analyzing issues, users, and projects, as well as generating statistics such as issue trends and assignment distributions. You can query for lists or details, filter by project or user, and request aggregated views to support dashboards or automation workflows. The server also supports performance optimizations like selecting only needed fields, paging results, and optional data compression to handle large datasets. This makes it suitable for building integration layers, analytics dashboards, or chat-based assistants that need structured data from MantisBT via MCP.

To use the tools, connect to the MCP stream provided by the server and invoke methods such as get_issues, get_issue_by_id, get_user, get_projects, get_issue_statistics, get_assignment_statistics, and get_users. Each method accepts specific parameters (e.g., projectId, statusId, handlerId, search, pageSize, page, select for issues; issueId for a single issue; username for users) to tailor the response. The statistics endpoints enable grouped analyses by status, priority, severity, handler, or reporter and allow period filtering (all, today, week, month). Ensure your environment variables (MANTIS_API_URL and MANTIS_API_KEY) are correctly configured so the MCP server can authenticate with your MantisBT instance. The server’s logging level can be adjusted via LOG_LEVEL to aid in monitoring or debugging during integration.

Prerequisites:

• Node.js and npm installed on your machine
• Access to a MantisBT instance with REST API enabled and a generated API key

1. Install the MCP server globally (optional but recommended for easy access):

npm install -g mantis-mcp-server

2. If you prefer to run without global installation, you can run the MCP server via npx (as shown below) or integrate it into your automation pipeline.

3. Create a configuration file for your environment (examples below):

• macOS/Linux (using npx to fetch latest):

# Ensure required environment variables are set in your shell or a .env file
export MANTIS_API_URL="https://your-mantis-instance.com/api/rest"
export MANTIS_API_KEY="your_api_key_here"
export NODE_ENV="production"
export LOG_LEVEL="info"

npx -y mantis-mcp-server@latest

• Windows (using a global mcp.json configuration example provided in the README):

{
  "mcpServers": {
    "mantis-mcp-server": {
      "type": "stdio",
      "command": "cmd",
      "args": [
        "/c",
        "node",
        "%APPDATA%\\npm\\node_modules\\mantis-mcp-server\\dist\\index.js"
      ],
      "env": {
        "MANTIS_API_URL": "YOUR_MANTIS_API_URL",
        "MANTIS_API_KEY": "YOUR_MANTIS_API_KEY",
        "NODE_ENV": "production",
        "LOG_LEVEL": "info"
      }
    }
  }
}

4. Run the server with your configuration. If using the npx approach, the command will resolve and start the MCP server and await MCP protocol connections. Ensure the MANTIS_API_URL and MANTIS_API_KEY are valid for authentication against your MantisBT instance.

Tips and common issues:

• Ensure MANTIS_API_URL uses the REST API endpoint and that the API key has the necessary permissions for the requested operations.
• If you modify NODE_ENV or LOG_LEVEL, restart the MCP server for changes to take effect.
• When handling large datasets, enable and test the paging (pageSize/page) and field selection (select) to minimize response payloads.
• If you encounter network or authentication errors, verify network access to the MantisBT instance and double-check the API key scope.
• For production deployments, consider enabling file-based logging and rotating log files to avoid disk space issues.