
Goodday
Connects to Goodday project management platform to manage projects, tasks, sprints, and users. Includes semantic search capabilities for finding tasks and documents using natural language queries.
Integrates with Goodday's project management platform to provide task, project, sprint, and user management capabilities with semantic search through vector database integration for automated workflow management and analytics.
What it does
- Manage projects and task assignments
- Create and update tasks with comments
- Generate sprint summaries and reports
- Search tasks using natural language queries
- Retrieve user information and assignments
- Track task status and project progress
Best for
About Goodday
Goodday is a community-built MCP server published by cdmx-in that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate Goodday with top project management software for seamless task, sprint, and user tracking, with analytics and It is categorized under productivity.
How to install
You can install Goodday in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
Goodday is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
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
- Copy
openwebui/goodday_openwebui_complete_tool.pyto your OpenWebUI tools directory - Configure the valves with your API credentials:
api_key: Your Goodday API tokensearch_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
-
Install UV (if not already installed):
curl -LsSf https://astral.sh/uv/install.sh | sh -
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
-
Set up environment variables: Create a
.envfile in your project root or export the variable:export GOODDAY_API_TOKEN=your_goodday_api_token_hereTo 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
-
Configure Claude Desktop by editing your configuration file:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
- macOS:
-
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" } } } } -
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
| Variable | Description | Required |
|---|---|---|
GOODDAY_API_TOKEN | Your Goodday API token | Yes |
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:
-
Add the tool function in
goodday_mcp/main.pyusing 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" -
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
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests if applicable
- Submit a pull request
License
This project is licensed under the MIT License - see the LICENSE file for details.
Support
For issues related to:
- MCP Server: Create an issue in this repository
- Goodday API: Refer to Goodday API documentation
- MCP Protocol: Refer to MCP documentation
Changelog
v1.1.0 (Current)
- Enhanced Task Management: Added
get_task_detailsandget_task_messagesfor comprehensive task information - Sprint Management: Added
get_goodday_sprint_tasksandget_goodday_sprint_summaryfor sprint tracking - Smart Query Interface: Added
get_goodday_smart_queryfor natural language project queries - Semantic Search: Added
search_goodday_taskswith VectorDB integration for intelligent task search - Document Management: Added
search_project_documentsa
README truncated. View full README on GitHub.
Alternatives
Related Skills
Browse all skillsLeveraging AI coding assistants and tools to boost development productivity, while maintaining oversight to ensure quality results.
Creates educational Teams channel posts for internal knowledge sharing about Claude Code features, tools, and best practices. Applies when writing posts, announcements, or documentation to teach colleagues effective Claude Code usage, announce new features, share productivity tips, or document lessons learned. Provides templates, writing guidelines, and structured approaches emphasizing concrete examples, underlying principles, and connections to best practices like context engineering. Activates for content involving Teams posts, channel announcements, feature documentation, or tip sharing.
Expert methodology for defining, tracking, and interpreting engineering performance metrics including DORA, team health, productivity, and executive reporting.
This skill should be used whenever users request personal assistance tasks such as schedule management, task tracking, reminder setting, habit monitoring, productivity advice, time management, or any query requiring personalized responses based on user preferences and context. On first use, collects comprehensive user information including schedule, working habits, preferences, goals, and routines. Maintains an intelligent database that automatically organizes and prioritizes information, keeping relevant data and discarding outdated context.
Boost your productivity with automated task management
Optimize local development workflow with Cursor. Triggers on "cursor workflow", "cursor development loop", "cursor productivity", "cursor daily workflow". Use when working with cursor local dev loop functionality. Trigger with phrases like "cursor local dev loop", "cursor loop", "cursor".