- Best for
- task creation and management, task retrieval with filtering, bulk task operations
- Type
- MCP Server · Free
- Score
- 23/100
- Best alternative
- AWS MCP Servers
- Agent-compatible
- Yes — MCP protocol
Capabilities3 decomposed
task creation and management
Medium confidenceThis capability allows users to create, update, and delete tasks in Google Tasks through a structured API interface. It leverages the Model-Context-Protocol (MCP) to facilitate seamless communication between the client and Google Tasks, ensuring that task data is synchronized in real-time. The implementation uses a RESTful approach to interact with Google Tasks, making it easy to integrate into various applications and workflows.
Utilizes the Model-Context-Protocol to maintain state and context across multiple task operations, enhancing the user experience by reducing the need for repetitive API calls.
More efficient than traditional REST clients by maintaining context, which reduces the number of API calls needed for task management.
task retrieval with filtering
Medium confidenceThis capability enables users to retrieve tasks from Google Tasks with advanced filtering options based on parameters like due date, completion status, and task labels. It employs a query-building mechanism that constructs API requests dynamically based on user-defined filters, allowing for precise data retrieval. The integration with Google Tasks API ensures that users receive up-to-date task information.
Incorporates a dynamic query-building engine that allows for complex filtering, which is not commonly found in simpler integrations with Google Tasks.
Offers more flexible and powerful filtering options compared to standard Google Tasks API clients, enabling tailored task retrieval.
bulk task operations
Medium confidenceThis capability allows users to perform bulk operations on tasks, such as creating, updating, or deleting multiple tasks in a single API call. It uses batch processing techniques to minimize the number of requests sent to the Google Tasks API, which enhances performance and reduces latency. The implementation ensures that all operations are executed atomically, providing a consistent state for task data.
Implements batch processing to optimize API calls, reducing overhead and improving performance for bulk task operations.
More efficient than individual task operations by minimizing API calls, which is crucial for applications managing large task datasets.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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** - Full implementation of Todoist Rest API for MCP server
Best For
- ✓developers building task management applications
- ✓teams integrating Google Tasks into their workflows
- ✓developers needing to display task lists in applications
- ✓teams analyzing task completion rates
- ✓developers needing efficient task management solutions
- ✓teams managing large sets of tasks
Known Limitations
- ⚠Limited to Google Tasks API capabilities; cannot handle non-standard task attributes
- ⚠Rate limiting imposed by Google API may affect performance
- ⚠Dependent on Google Tasks API's filtering capabilities; complex queries may not be supported
- ⚠Performance may vary based on the number of tasks in the user's account
- ⚠Batch size is limited by Google API constraints; cannot exceed 100 tasks per request
- ⚠Error handling for partial failures can be complex
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
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MCP server: mcp-googletasks
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