7df58df199
This commit introduces significant enhancements and refactoring to the Task Master CLI, focusing on improved testing, integration with Perplexity AI for research-backed task updates, and core logic refactoring for better maintainability and functionality.
**Testing Infrastructure Setup:**
- Implemented Jest as the primary testing framework, setting up a comprehensive testing environment.
- Added new test scripts to including , , and for streamlined testing workflows.
- Integrated necessary devDependencies for testing, such as , , , , and , to support unit, integration, and end-to-end testing.
**Dependency Updates:**
- Updated and to reflect the latest dependency versions, ensuring project stability and access to the newest features and security patches.
- Upgraded to version 0.9.16 and usage: openai [-h] [-v] [-b API_BASE] [-k API_KEY] [-p PROXY [PROXY ...]]
[-o ORGANIZATION] [-t {openai,azure}]
[--api-version API_VERSION] [--azure-endpoint AZURE_ENDPOINT]
[--azure-ad-token AZURE_AD_TOKEN] [-V]
{api,tools,migrate,grit} ...
positional arguments:
{api,tools,migrate,grit}
api Direct API calls
tools Client side tools for convenience
options:
-h, --help show this help message and exit
-v, --verbose Set verbosity.
-b, --api-base API_BASE
What API base url to use.
-k, --api-key API_KEY
What API key to use.
-p, --proxy PROXY [PROXY ...]
What proxy to use.
-o, --organization ORGANIZATION
Which organization to run as (will use your default
organization if not specified)
-t, --api-type {openai,azure}
The backend API to call, must be `openai` or `azure`
--api-version API_VERSION
The Azure API version, e.g.
'https://learn.microsoft.com/en-us/azure/ai-
services/openai/reference#rest-api-versioning'
--azure-endpoint AZURE_ENDPOINT
The Azure endpoint, e.g.
'https://endpoint.openai.azure.com'
--azure-ad-token AZURE_AD_TOKEN
A token from Azure Active Directory,
https://www.microsoft.com/en-
us/security/business/identity-access/microsoft-entra-
id
-V, --version show program's version number and exit to 4.89.0.
- Added dependency (version 2.3.0) and updated related dependencies to their latest versions.
**Perplexity AI Integration for Research-Backed Updates:**
- Introduced an option to leverage Perplexity AI for task updates, enabling research-backed enhancements to task details.
- Implemented logic to initialize a Perplexity AI client if the environment variable is available.
- Modified the function to accept a parameter, allowing dynamic selection between Perplexity AI and Claude AI for task updates based on API key availability and user preference.
- Enhanced to handle responses from Perplexity AI and update tasks accordingly, including improved error handling and logging for robust operation.
**Core Logic Refactoring and Improvements:**
- Refactored the function to utilize task IDs instead of dependency IDs, ensuring consistency and clarity in dependency management.
- Implemented a new function to rigorously check for both circular dependencies and self-dependencies within tasks, improving task relationship integrity.
- Enhanced UI elements in :
- Refactored to incorporate icons for different task statuses and utilize a object for color mapping, improving visual representation of task status.
- Updated to display colored complexity scores with emojis, providing a more intuitive and visually appealing representation of task complexity.
- Refactored the task data structure creation and validation process:
- Updated the JSON Schema for to reflect a more streamlined and efficient task structure.
- Implemented Task Model Classes for better data modeling and type safety.
- Improved File System Operations for task data management.
- Developed robust Validation Functions and an Error Handling System to ensure data integrity and application stability.
**Testing Guidelines Implementation:**
- Implemented guidelines for writing testable code when developing new features, promoting a test-driven development approach.
- Added testing requirements and best practices for unit, integration, and edge case testing to ensure comprehensive test coverage.
- Updated the development workflow to mandate writing tests before proceeding with configuration and documentation updates, reinforcing the importance of testing throughout the development lifecycle.
This commit collectively enhances the Task Master CLI's reliability, functionality, and developer experience through improved testing practices, AI-powered research capabilities, and a more robust and maintainable codebase.
59 lines
2.9 KiB
Plaintext
59 lines
2.9 KiB
Plaintext
# Task ID: 23
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# Title: Implement MCP (Model Context Protocol) Server Functionality for Task Master
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# Status: pending
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# Dependencies: ⏱️ 22 (in-progress)
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# Priority: medium
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# Description: Extend Task Master to function as an MCP server, allowing it to provide context management services to other applications following the Model Context Protocol specification.
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# Details:
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This task involves implementing the Model Context Protocol server capabilities within Task Master. The implementation should:
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1. Create a new module `mcp-server.js` that implements the core MCP server functionality
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2. Implement the required MCP endpoints:
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- `/context` - For retrieving and updating context
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- `/models` - For listing available models
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- `/execute` - For executing operations with context
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3. Develop a context management system that can:
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- Store and retrieve context data efficiently
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- Handle context windowing and truncation when limits are reached
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- Support context metadata and tagging
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4. Add authentication and authorization mechanisms for MCP clients
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5. Implement proper error handling and response formatting according to MCP specifications
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6. Create configuration options in Task Master to enable/disable the MCP server functionality
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7. Add documentation for how to use Task Master as an MCP server
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8. Ensure the implementation is compatible with existing MCP clients
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9. Optimize for performance, especially for context retrieval operations
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10. Add logging for MCP server operations
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The implementation should follow RESTful API design principles and should be able to handle concurrent requests from multiple clients.
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# Test Strategy:
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Testing for the MCP server functionality should include:
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1. Unit tests:
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- Test each MCP endpoint handler function independently
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- Verify context storage and retrieval mechanisms
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- Test authentication and authorization logic
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- Validate error handling for various failure scenarios
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2. Integration tests:
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- Set up a test MCP server instance
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- Test complete request/response cycles for each endpoint
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- Verify context persistence across multiple requests
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- Test with various payload sizes and content types
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3. Compatibility tests:
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- Test with existing MCP client libraries
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- Verify compliance with the MCP specification
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- Ensure backward compatibility with any MCP versions supported
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4. Performance tests:
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- Measure response times for context operations with various context sizes
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- Test concurrent request handling
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- Verify memory usage remains within acceptable limits during extended operation
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5. Security tests:
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- Verify authentication mechanisms cannot be bypassed
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- Test for common API vulnerabilities (injection, CSRF, etc.)
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All tests should be automated and included in the CI/CD pipeline. Documentation should include examples of how to test the MCP server functionality manually using tools like curl or Postman.
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