What is Google Tasks Api

google-tasks-api is an API that allows developers to interact with Google Tasks, enabling the creation, retrieval, updating, and deletion of task lists and tasks within those lists.

Use cases

Use cases for google-tasks-api include building applications that help users manage their daily tasks, integrating task management into existing software solutions, and automating task-related workflows.

How to use

To use google-tasks-api, developers need to clone the repository, install the required dependencies using pip, and then run the MCP server using the provided scripts. The API can be accessed via the specified OpenAPI URL.

Key features

Key features of google-tasks-api include task management capabilities, support for multiple transport modes (e.g., stdio, sse), and integration with Python development tools for linting, testing, and static analysis.

Where to use

google-tasks-api can be used in various domains including personal productivity applications, project management tools, and any software that requires task management functionalities.

MCP Server

This project is an MCP (Multi-Agent Conversation Protocol) Server for the given OpenAPI URL - https://api.apis.guru/v2/specs/googleapis.com/tasks/v1/openapi.json, auto-generated using AG2’s MCP builder.

Prerequisites

• Python 3.9+
• pip and uv

Installation

1. Clone the repository:

   git clone <repository-url>
   cd mcp-server
   

2. Install dependencies:
   The .devcontainer/setup.sh script handles installing dependencies using pip install -e ".[dev]". If you are not using the dev container, you can run this command manually.

   pip install -e ".[dev]"
   

   Alternatively, you can use uv:

   uv pip install --editable ".[dev]"
   

Development

This project uses ruff for linting and formatting, mypy for static type checking, and pytest for testing.

Linting and Formatting

To check for linting issues:

ruff check

To format the code:

ruff format

These commands are also available via the scripts/lint.sh script.

Static Analysis

To run static analysis (mypy, bandit, semgrep):

./scripts/static-analysis.sh

This script is also configured as a pre-commit hook in .pre-commit-config.yaml.

Running Tests

To run tests with coverage:

./scripts/test.sh

This will run pytest and generate a coverage report. For a combined report and cleanup, you can use:

./scripts/test-cov.sh

Pre-commit Hooks

This project uses pre-commit hooks defined in .pre-commit-config.yaml. To install the hooks:

pre-commit install

The hooks will run automatically before each commit.

Running the Server

The MCP server can be started using the mcp_server/main.py script. It supports different transport modes (e.g., stdio, sse).

To start the server (e.g., in stdio mode):

python mcp_server/main.py stdio

The server can be configured using environment variables:

• CONFIG_PATH: Path to a JSON configuration file (e.g., mcp_server/mcp_config.json).
• CONFIG: A JSON string containing the configuration.
• SECURITY: Environment variables for security parameters (e.g., API keys).

Refer to the if __name__ == "__main__": block in mcp_server/main.py for details on how these are loaded.

The tests/test_mcp_server.py file demonstrates how to start and interact with the server programmatically for testing.

Building and Publishing

This project uses Hatch for building and publishing.
To build the project:

hatch build

To publish the project:

hatch publish

These commands are also available via the scripts/publish.sh script.