mcpField
G
Goodday Mcp

Goodday Mcp

Local
github.com/cdmx-inProductivity2Updated 58w ago

About Goodday Mcp

Goodday Mcp is an MCP server for accessing the Goodday project management platform through API v2. It serves teams and AI clients that need to query or update projects, tasks, users, sprints, and project documents, with search and natural-language query tools.

Key features

  1. 01Retrieve and manage projects and tasks
  2. 02Access users, sprints, and task details
  3. 03Search tasks and project documents
  4. 04Run natural-language project queries
  5. 05Create tasks and update status or comments

Use cases

  1. 01Review a user's assigned or action-required tasks
  2. 02Summarize sprint tasks and status distribution
  3. 03Search task records or project documents
  4. 04Create tasks and post comments through Goodday

MCP support

The server exposes Goodday project, task, user, sprint, search, and document operations as tools to MCP clients. It communicates over stdio and requires a Goodday API token.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Goodday MCP Server

A Model Context Protocol (MCP) server for integrating with Goodday project management platform. This server provides tools for managing projects, tasks, and users through the Goodday API v2.

Features

Project Management
  • get_projects: Retrieve list of projects (with options for archived and root-only filtering)
  • get_project: Get detailed information about a specific project
  • create_project: Create new projects with customizable templates and settings
  • get_project_users: Get users associated with a specific project
Task Management
  • get_project_tasks: Retrieve tasks from specific projects (with options for closed tasks and subfolders)
  • get_user_assigned_tasks: Get tasks assigned to a specific user
  • get_user_action_required_tasks: Get action-required tasks for a user
  • get_task: Get detailed information about a specific task
  • get_task_details: Get comprehensive task details including subtasks, custom fields, and full metadata
  • get_task_messages: Retrieve all messages/comments for a specific task
  • create_task: Create new tasks with full customization (subtasks, assignments, dates, priorities)
  • update_task_status: Update task status with optional comments
  • add_task_comment: Add comments to tasks
Sprint Management
  • get_goodday_sprint_tasks: Get tasks from specific sprints by project name and sprint name/number
  • get_goodday_sprint_summary: Generate comprehensive sprint summaries with task details, status distribution, and key metrics
User Management
  • get_users: Retrieve list of organization users
  • get_user: Get detailed information about a specific user
Smart Query & Search
  • get_goodday_smart_query: Natural language interface for common project management queries
  • search_goodday_tasks: Semantic search across tasks using VectorDB backend
  • search_project_documents: Search for documents within specific projects
  • get_document_content: Retrieve full content of specific documents

OpenWebUI Integration

This package also includes an OpenWebUI tool that provides a complete interface for Goodday project management directly in chat interfaces. The OpenWebUI tool includes:

Features
  • Project Management: Get projects, project tasks, and project details
  • Sprint Management: Get tasks from specific sprints by name/number, comprehensive sprint summaries
  • User Management: Get tasks assigned to specific users, user details
  • Task Details: Get comprehensive task information including subtasks, custom fields, and metadata
  • Task Messages: Retrieve all messages and comments for tasks
  • Smart Query: Natural language interface for common project management requests
  • Semantic Search: Search across tasks using VectorDB backend with embeddings
  • Document Management: Search project documents and retrieve document content
  • Advanced Filtering: Support for archived projects, closed tasks, subfolders, and more
Setup
  1. Copy openwebui/goodday_openwebui_complete_tool.py to your OpenWebUI tools directory
  2. Configure the valves with your API credentials:
    • api_key: Your Goodday API token
    • search_url: Your VectorDB search endpoint (optional)
    • bearer_token: Bearer token for search API (optional)
Vector Database Setup (Optional)

For semantic search functionality, you can set up a vector database using the provided n8n workflow (openwebui/n8n-workflow-goodday-vectordb.json). This workflow:

  • Fetches all Goodday projects and tasks
  • Extracts task messages and content
  • Creates embeddings using Ollama
  • Stores in Qdrant vector database
  • Provides search API endpoint

See openwebui/OPENWEBUI_TOOL_README.md for detailed usage instructions.

Installation

From PyPI (Recommended)
pip install goodday-mcp
From Source
Prerequisites
  • Python 3.10 or higher
  • UV package manager (recommended) or pip
  • Goodday API token
Setup with UV
  1. Install UV (if not already installed):

    curl -LsSf https://astral.sh/uv/install.sh | sh
    
  2. Clone and set up the project:

    git clone https://github.com/cdmx1/goodday-mcp.git
    cd goodday-mcp
    
    # Create virtual environment and install dependencies
    uv venv
    source .venv/bin/activate  # On Windows: .venv\Scripts\activate
    uv sync
    
Setup with pip
git clone https://github.com/cdmx1/goodday-mcp.git
cd goodday-mcp
pip install -e .
Configuration
  1. Set up environment variables: Create a .env file in your project root or export the variable:

    export GOODDAY_API_TOKEN=your_goodday_api_token_here
    

    To get your Goodday API token:

    • Go to your Goodday organization
    • Navigate to Settings → API
    • Click the generate button to create a new token

Usage

Running the Server Standalone

If installed from PyPI:

goodday-mcp

If running from source with UV:

uv run goodday-mcp
Using with Claude Desktop
  1. Configure Claude Desktop by editing your configuration file:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
  2. Add the server configuration:

    Option A: If installed from PyPI:

    {
      "mcpServers": {
        "goodday": {
          "command": "goodday-mcp",
          "env": {
            "GOODDAY_API_TOKEN": "your_goodday_api_token_here"
          }
        }
      }
    }
    

    Option B: If running from source:

    {
      "mcpServers": {
        "goodday": {
          "command": "uv",
          "args": ["run", "goodday-mcp"],
          "env": {
            "GOODDAY_API_TOKEN": "your_goodday_api_token_here"
          }
        }
      }
    }
    
  3. Restart Claude Desktop to load the new server.

Using with Other MCP Clients

The server communicates via stdio transport and can be integrated with any MCP-compatible client. Refer to the MCP documentation for client-specific integration instructions.

API Reference

Environment Variables
VariableDescriptionRequired
GOODDAY_API_TOKENYour Goodday API tokenYes
Tool Examples
Get Projects
# Get all active projects
get_projects()

# Get archived projects
get_projects(archived=True)

# Get only root-level projects
get_projects(root_only=True)
Create a Task
create_task(
    project_id="project_123",
    title="Implement new feature",
    from_user_id="user_456",
    message="Detailed description of the task",
    to_user_id="user_789",
    deadline="2025-06-30",
    priority=5
)
Update Task Status
update_task_status(
    task_id="task_123",
    user_id="user_456",
    status_id="status_completed",
    message="Task completed successfully"
)

Data Formats

Date Format

All dates should be provided in YYYY-MM-DD format (e.g., 2025-06-16).

Priority Levels
  • 1-10: Normal priority levels
  • 50: Blocker
  • 100: Emergency
Project Colors

Project colors are specified as integers from 1-24, corresponding to Goodday's color palette.

Error Handling

The server includes comprehensive error handling:

  • Authentication errors: When API token is missing or invalid
  • Network errors: When Goodday API is unreachable
  • Validation errors: When required parameters are missing
  • Permission errors: When user lacks permissions for requested operations

All errors are returned as descriptive strings to help with troubleshooting.

Development

Project Structure
goodday-mcp/
├── goodday_mcp/         # Main package directory
│   ├── __init__.py      # Package initialization
│   └── main.py          # Main MCP server implementation
├── pyproject.toml       # Project configuration and dependencies
├── README.md           # This file
├── LICENSE             # MIT license
├── uv.lock            # Dependency lock file
└── .env               # Environment variables (create this)
Adding New Tools

To add new tools to the server:

  1. Add the tool function in goodday_mcp/main.py using the @mcp.tool() decorator:

    @mcp.tool()
    async def your_new_tool(param1: str, param2: Optional[int] = None) -> str:
        """Description of what the tool does.
        
        Args:
            param1: Description of parameter 1
            param2: Description of optional parameter 2
        """
        # Implementation here
        return "Result"
    
  2. Test the tool by running the server and testing with an MCP client.

Testing

Test the server by running it directly:

# If installed from PyPI
goodday-mcp

# If running from source
uv run goodday-mcp

The server will start and wait for MCP protocol messages via stdin/stdout.

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests if applicable
  5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For issues related to:

Changelog

v1.1.0 (Current)
  • Enhanced Task Management: Added get_task_details and get_task_messages for comprehensive task information
  • Sprint Management: Added get_goodday_sprint_tasks and get_goodday_sprint_summary for sprint tracking
  • Smart Query Interface: Added get_goodday_smart_query for natural language project queries
  • Semantic Search: Added search_goodday_tasks with VectorDB integration for intelligent task search
  • Document Management: Added search_project_documents and get_document_content for document handling
  • Improved Error Handling: Enhanced error messages and status reporting
  • Advanced Filtering: Support for archived projects, closed tasks, and subfolder inclusion
v1.0.0
  • Initial release
  • Full project management capabilities
  • Task management with comments and status updates
  • User management
  • Comprehensive error handling
  • UV support with modern Python packaging

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.