* feat: Add .nvmrc and align engines to Node 20
* chore: set nvm to 22, engines to 18
* chore: format
* chore: add changeset
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Co-authored-by: Amir Golan <amirgolan@Amirs-MacBook-Pro.local>
* fix: claude-4 not having the right max_tokens
* feat: add bedrock support
* chore: fix package-lock.json
* fix: rename baseUrl to baseURL
* feat: add azure support
* fix: final touches of azure integration
* feat: add google vertex provider
* chore: fix tests and refactor task-manager.test.js
* chore: move task 92 to 94
* feat(config): Implement TASK_MASTER_PROJECT_ROOT support for project root resolution
- Added support for the TASK_MASTER_PROJECT_ROOT environment variable in MCP configuration, establishing a clear precedence order for project root resolution.
- Updated utility functions to prioritize the environment variable, followed by args.projectRoot and session-based resolution.
- Enhanced error handling and logging for project root determination.
- Introduced new tasks for comprehensive testing and documentation updates related to the new configuration options.
* chore: fix CI issues
* fix(add-task): fixes an isse in which stdout leaks out of add-task causing the mcp server to crash if used.
* chore: add changeset
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Co-authored-by: Ralph Khreish <35776126+Crunchyman-ralph@users.noreply.github.com>
- New MCP Tool: research tool enables AI-powered research with project context
- Context Integration: Supports task IDs, file paths, custom context, and project tree
- Fuzzy Task Discovery: Automatically finds relevant tasks using semantic search
- Token Management: Detailed token counting and breakdown by context type
- Multiple Detail Levels: Support for low, medium, and high detail research responses
- Telemetry Integration: Full cost tracking and usage analytics
- Direct Function: researchDirect with comprehensive parameter validation
- Silent Mode: Prevents console output interference with MCP JSON responses
- Error Handling: Robust error handling with proper MCP response formatting
This completes subtasks 94.5 (Direct Function) and 94.6 (MCP Tool) for the research command implementation, providing a powerful research interface for integrated development environments like Cursor.
Updated documentation across taskmaster.mdc, README.md, command-reference.md, examples.md, tutorial.md, and docs/README.md to highlight research capabilities and usage patterns.
- Enhanced get-task/show command to support comma-separated task IDs for efficient batch operations.
- New features include multiple task retrieval, smart display logic, interactive action menu with batch operations, MCP array response for AI agent efficiency, and support for mixed parent tasks and subtasks.
- Implementation includes updated CLI show command, enhanced MCP get_task tool, modified showTaskDirect function, and maintained full backward compatibility.
- Documentation updated across all relevant files.
Benefits include faster context gathering for AI agents, improved workflow with interactive batch operations, better UX with responsive layout, and enhanced API efficiency.
- Enhanced fuzzy search to include subtasks in discovery - Added interactive follow-up question functionality using inquirer
- Improved context discovery by including both tasks and subtasks
- Follow-up option for research with default to 'n' for quick workflow
- Fixed parent-to-parent task moves where original task would remain as duplicate
- Fixed moving tasks to become subtasks of empty parents (validation errors)
- Fixed moving subtasks between different parent tasks
- Improved comma-separated batch moves with proper error handling
- Updated MCP tool to use core logic instead of custom implementation
- Resolves task duplication issues and enables proper task hierarchy reorganization
- Create comprehensive context_gathering.mdc rule documenting ContextGatherer utility patterns, FuzzyTaskSearch integration, token breakdown display, code block syntax highlighting, and enhanced result display patterns
- Update new_features.mdc to include context gathering step
- Update commands.mdc with context-aware command pattern
- Update ui.mdc with enhanced display patterns and syntax highlighting
- Update utilities.mdc to document new context gathering utilities
- Update glossary.mdc to include new context_gathering rule
- Establishes standardized patterns for building intelligent, context-aware commands that can leverage project knowledge for better AI assistance
Adds a new CLI command and MCP tool to reorganize tasks and subtasks within the hierarchy. Features include:
- Moving tasks between different positions in the task list
- Converting tasks to subtasks and vice versa
- Moving subtasks between parents
- Moving multiple tasks at once with comma-separated IDs
- Creating placeholder tasks when moving to new IDs
- Validation to prevent accidental data loss
This is particularly useful for resolving merge conflicts when multiple team members create tasks on different branches.
Enhance analyze-complexity to support analyzing specific tasks by ID or range:
- Add --id option for comma-separated task IDs
- Add --from/--to options for analyzing tasks within a range
- Implement intelligent merging with existing reports
- Update CLI, MCP tools, and direct functions for consistent support
- Add changeset documenting the feature
This commit implements automatic tasks.json file creation when it doesn't exist:
- When tasks.json is missing or invalid, create a new one with { tasks: [] }
- Allows adding tasks immediately after initializing a project without parsing a PRD
- Replaces error with informative feedback about file creation
- Enables smoother workflow for new projects or directories
This change improves user experience by removing the requirement to parse a PRD
before adding the first task to a newly initialized project. Closes#494
This commit significantly improves the functionality by implementing
fuzzy semantic search to find contextually relevant dependencies:
- Add Fuse.js for powerful fuzzy search capability with weighted multi-field matching
- Implement score-based relevance ranking with high/medium relevance tiers
- Enhance context generation to include detailed information about similar tasks
- Fix context shadowing issue that prevented detailed task information from
reaching the AI model
- Add informative CLI output showing semantic search results and dependency patterns
- Improve formatting of dependency information in prompts with task titles
The result is that newly created tasks are automatically placed within the correct
dependency structure without manual intervention, with the AI having much better
context about which tasks are most relevant to the new one being created.
This significantly improves the user experience by reducing the need to manually
update task dependencies after creation, all without increasing token usage or costs.
