Config Structure Changes and Gateway Integration
## Configuration Structure Changes
- Restructured .taskmasterconfig to use 'account' section for user settings
- Moved userId, userEmail, mode, telemetryEnabled from global to account section
- API keys remain isolated in .env file (not accessible to AI)
- Enhanced getUserId() to always return value, never null (sets default '1234567890')
## Gateway Integration Enhancements
- Updated registerUserWithGateway() to accept both email and userId parameters
- Enhanced /auth/init endpoint integration for existing user validation
- API key updates automatically written to .env during registration process
- Improved user identification and validation flow
## Code Updates for New Structure
- Fixed config-manager.js getter functions for account section access
- Updated user-management.js to use config.account.userId/mode
- Modified telemetry-submission.js to read from account section
- Added getTelemetryEnabled() function with proper account section access
- Enhanced telemetry configuration reading with new structure
## Comprehensive Test Updates
- Updated integration tests (init-config.test.js) for new config structure
- Fixed unit tests (config-manager.test.js) with updated default config
- Updated telemetry tests (telemetry-submission.test.js) for account structure
- Added missing getTelemetryEnabled mock to ai-services-unified.test.js
- Fixed all test expectations to use config.account.* instead of config.global.*
- Removed references to deprecated config.subscription object
## Configuration Access Consistency
- Standardized configuration access patterns across entire codebase
- Clean separation: user settings in account, API keys in .env, models/global in respective sections
- All tests passing with new configuration structure
- Maintained backward compatibility during transition
Changes support enhanced telemetry system with proper user management and gateway integration while maintaining security through API key isolation.
- Hardcoded gateway endpoint to http://localhost:4444/api/v1/telemetry
- Updated credential handling to use config-based approach (not env vars)
- Added registerUserWithGateway() function for user registration/lookup
- Enhanced init.js with hosted gateway setup option and configureTelemetrySettings()
- Updated all 10 tests to reflect new architecture - all passing
- Security features maintained: sensitive data filtering, Bearer token auth
- Ready for ai-services-unified.js integration in subtask 90.3
- Implement secure telemetry submission service
- Created scripts/modules/telemetry-submission.js with submitTelemetryData function
- Implemented secure filtering: removes commandArgs and fullOutput before remote submission
- Added comprehensive validation using Zod schema for telemetry data integrity
- Implemented exponential backoff retry logic (3 attempts max) with smart retry decisions
- Added graceful error handling that never blocks execution
- Respects user opt-out preferences via config.telemetryEnabled
- Configured for localhost testing endpoint (http://localhost:4444/api/v1/telemetry) for now
- Added comprehensive test coverage with 6/6 passing tests covering all scenarios
- Includes submitTelemetryDataAsync for fire-and-forget submissions
- Implement secure telemetry capture with filtering - Enhanced ai-services-unified.js to capture commandArgs and fullOutput in telemetry - Added filterSensitiveTelemetryData() function to prevent sensitive data exposure - Updated processMCPResponseData() to filter telemetry before sending to MCP clients - Verified CLI displayAiUsageSummary() only shows safe fields - Added comprehensive test coverage with 4 passing tests - Resolved critical security issue: API keys and sensitive data now filtered from responses
- Fixed logic in moveTaskToNewId function that was incorrectly treating task-to-task moves as subtask creation instead of task replacement
- Updated moveTaskToNewId to properly handle replacing existing destination tasks instead of just placeholders
- The move command now correctly replaces destination tasks and cleans up properly without leaving duplicates
- Task Management: Moved task 93 (Google Vertex AI Provider) to position 88, Moved task 94 (Azure OpenAI Provider) to position 89, Updated task dependencies and regenerated task files, Cleaned up orphaned task files automatically
- All important validations remain in place: Prevents moving tasks to themselves, Prevents moving parent tasks to their own subtasks, Prevents circular dependencies
- Resolves the issue where moving tasks would leave both source and destination tasks in tasks.json and file system
* 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
- 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.
- 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
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.
This commit introduces several improvements to AI interactions and
task management functionalities:
- AI Provider Enhancements (for Telemetry & Robustness):
- :
- Added a check in to ensure
is a string, throwing an error if not. This prevents downstream
errors (e.g., in ).
- , , :
- Standardized return structures for their respective
and functions to consistently include /
and fields. This aligns them with other providers (like
Anthropic, Google, Perplexity) for consistent telemetry data
collection, as part of implementing subtask 77.14 and similar work.
- Task Expansion ():
- Updated to be more explicit
about using an empty array for empty to
better guide AI output.
- Implemented a pre-emptive cleanup step in
to replace malformed with
before JSON parsing. This improves resilience to AI output quirks,
particularly observed with Perplexity.
- Adjusts issue in commands.js where successfulRemovals would be undefined. It's properly invoked from the result variable now.
- Updates supported models for Gemini
These changes address issues observed during E2E tests, enhance the
reliability of AI-driven task analysis and expansion, and promote
consistent telemetry data across multiple AI providers.
This commit updates the Perplexity AI provider () to ensure its functions return data in a structure consistent with other providers and the expectations of the unified AI service layer ().
Specifically:
- now returns an object instead of only the text string.
- now returns an object instead of only the result object.
These changes ensure that can correctly extract both the primary AI-generated content and the token usage data for telemetry purposes when Perplexity models are used. This resolves issues encountered during E2E testing where complexity analysis (which can use Perplexity for its research role) failed due to unexpected response formats.
The function was already compliant.
This commit introduces two key improvements:
1. **Google Provider Telemetry:**
- Updated to include token usage data (, ) in the responses from and .
- This aligns the Google provider with others for consistent AI usage telemetry.
2. **Robust AI Object Response Handling:**
- Modified to more flexibly handle responses from .
- The add-task module now check for the AI-generated object in both and , improving compatibility with different AI provider response structures (e.g., Gemini).
These changes enhance the reliability of AI interactions, particularly with the Google provider, and ensure accurate telemetry collection.
This commit applies the standard telemetry pattern to the analyze-task-complexity command and its corresponding MCP tool.
Key Changes:
1. Core Logic (scripts/modules/task-manager/analyze-task-complexity.js):
- The call to generateTextService now includes commandName: 'analyze-complexity' and outputType.
- The full response { mainResult, telemetryData } is captured.
- mainResult (the AI-generated text) is used for parsing the complexity report JSON.
- If running in CLI mode (outputFormat === 'text'), displayAiUsageSummary is called with the telemetryData.
- The function now returns { report: ..., telemetryData: ... }.
2. Direct Function (mcp-server/src/core/direct-functions/analyze-task-complexity.js):
- The call to the core analyzeTaskComplexity function now passes the necessary context for telemetry (commandName, outputType).
- The successful response object now correctly extracts coreResult.telemetryData and includes it in the data.telemetryData field returned to the MCP client.
This commit applies the standard telemetry pattern to the update-subtask command and its corresponding MCP tool.
Key Changes:
1. Core Logic (scripts/modules/task-manager/update-subtask-by-id.js):
- The call to generateTextService now includes commandName: 'update-subtask' and outputType.
- The full response { mainResult, telemetryData } is captured.
- mainResult (the AI-generated text) is used for the appended content.
- If running in CLI mode (outputFormat === 'text'), displayAiUsageSummary is called with the telemetryData.
- The function now returns { updatedSubtask: ..., telemetryData: ... }.
2. Direct Function (mcp-server/src/core/direct-functions/update-subtask-by-id.js):
- The call to the core updateSubtaskById function now passes the necessary context for telemetry (commandName, outputType).
- The successful response object now correctly extracts coreResult.telemetryData and includes it in the data.telemetryData field returned to the MCP client.
This commit applies the standard telemetry pattern to the update-tasks command and its corresponding MCP tool.
Key Changes:
1. Core Logic (scripts/modules/task-manager/update-tasks.js):
- The call to generateTextService now includes commandName: 'update-tasks' and outputType.
- The full response { mainResult, telemetryData } is captured.
- mainResult (the AI-generated text) is used for parsing the updated task JSON.
- If running in CLI mode (outputFormat === 'text'), displayAiUsageSummary is called with the telemetryData.
- The function now returns { success: true, updatedTasks: ..., telemetryData: ... }.
2. Direct Function (mcp-server/src/core/direct-functions/update-tasks.js):
- The call to the core updateTasks function now passes the necessary context for telemetry (commandName, outputType).
- The successful response object now correctly extracts coreResult.telemetryData and includes it in the data.telemetryData field returned to the MCP client.
This commit applies the standard telemetry pattern to the command and its corresponding MCP tool.
Key Changes:
1. **Core Logic ():**
- The call to now includes and .
- The full response is captured.
- (the AI-generated text) is used for parsing the updated task JSON.
- If running in CLI mode (), is called with the .
- The function now returns .
2. **Direct Function ():**
- The call to the core function now passes the necessary context for telemetry (, ).
- The successful response object now correctly extracts and includes it in the field returned to the MCP client.
This commit implements AI usage telemetry for the `expand-all-tasks` command/tool and refactors its CLI output for clarity and consistency.
Key Changes:
1. **Telemetry Integration for `expand-all-tasks` (Subtask 77.8):**\n - The `expandAllTasks` core logic (`scripts/modules/task-manager/expand-all-tasks.js`) now calls the `expandTask` function for each eligible task and collects the individual `telemetryData` returned.\n - A new helper function `_aggregateTelemetry` (in `utils.js`) is used to sum up token counts and costs from all individual expansions into a single `telemetryData` object for the entire `expand-all` operation.\n - The `expandAllTasksDirect` wrapper (`mcp-server/src/core/direct-functions/expand-all-tasks.js`) now receives and passes this aggregated `telemetryData` in the MCP response.\n - For CLI usage, `displayAiUsageSummary` is called once with the aggregated telemetry.
2. **Improved CLI Output for `expand-all`:**\n - The `expandAllTasks` core function now handles displaying a final "Expansion Summary" box (showing Attempted, Expanded, Skipped, Failed counts) directly after the aggregated telemetry summary.\n - This consolidates all summary output within the core function for better flow and removes redundant logging from the command action in `scripts/modules/commands.js`.\n - The summary box border is green for success and red if any expansions failed.
3. **Code Refinements:**\n - Ensured `chalk` and `boxen` are imported in `expand-all-tasks.js` for the new summary box.\n - Minor adjustments to logging messages for clarity.
This commit integrates AI usage telemetry for the `expand-task` command/tool and resolves issues related to incorrect return type handling and logging.
Key Changes:
1. **Telemetry Integration for `expand-task` (Subtask 77.7):**\n - Applied the standard telemetry pattern to the `expandTask` core logic (`scripts/modules/task-manager/expand-task.js`) and the `expandTaskDirect` wrapper (`mcp-server/src/core/direct-functions/expand-task.js`).\n - AI service calls now pass `commandName` and `outputType`.\n - Core function returns `{ task, telemetryData }`.\n - Direct function correctly extracts `task` and passes `telemetryData` in the MCP response `data` field.\n - Telemetry summary is now displayed in the CLI output for the `expand` command.
2. **Fix AI Service Return Type Handling (`ai-services-unified.js`):**\n - Corrected the `_unifiedServiceRunner` function to properly handle the return objects from provider-specific functions (`generateText`, `generateObject`).\n - It now correctly extracts `providerResponse.text` or `providerResponse.object` into the `mainResult` field based on `serviceType`, resolving the "text.trim is not a function" error encountered during `expand-task`.
3. **Log Cleanup:**\n - Removed various redundant or excessive `console.log` statements across multiple files (as indicated by recent changes) to reduce noise and improve clarity, particularly for MCP interactions.
Implements AI usage telemetry capture and propagation for the command and MCP tool, following the established telemetry pattern.
Key changes:
- **Core ():**
- Modified the call to include and .
- Updated to receive from .
- Adjusted to return an object .
- Added a call to to show telemetry data in the CLI output when not in MCP mode.
- **Direct Function ():**
- Updated the call to the core function to pass , , and .
- Modified to correctly handle the new return structure from the core function.
- Ensures received from the core function is included in the field of the successful MCP response.
- **MCP Tool ():**
- No changes required; existing correctly passes through the object containing .
- **CLI Command ():**
- The command's action now relies on the core function to handle CLI success messages and telemetry display.
This ensures that AI usage for the functionality is tracked and can be displayed or logged as appropriate for both CLI and MCP interactions.
This commit introduces a standardized pattern for capturing and propagating AI usage telemetry (cost, tokens, model used) across the Task Master stack and applies it to the 'add-task' functionality.
Key changes include:
- **Telemetry Pattern Definition:**
- Added defining the integration pattern for core logic, direct functions, MCP tools, and CLI commands.
- Updated related rules (, ,
Usage: mcp [OPTIONS] COMMAND [ARGS]...
MCP development tools
╭─ Options ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --help Show this message and exit. │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ version Show the MCP version. │
│ dev Run a MCP server with the MCP Inspector. │
│ run Run a MCP server. │
│ install Install a MCP server in the Claude desktop app. │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯, , ) to reference the new telemetry rule.
- **Core Telemetry Implementation ():**
- Refactored the unified AI service to generate and return a object alongside the main AI result.
- Fixed an MCP server startup crash by removing redundant local loading of and instead using the imported from for cost calculations.
- Added to the object.
- ** Integration:**
- Modified (core) to receive from the AI service, return it, and call the new UI display function for CLI output.
- Updated to receive from the core function and include it in the payload of its response.
- Ensured (MCP tool) correctly passes the through via .
- Updated to correctly pass context (, ) to the core function and rely on it for CLI telemetry display.
- **UI Enhancement:**
- Added function to to show telemetry details in the CLI.
- **Project Management:**
- Added subtasks 77.6 through 77.12 to track the rollout of this telemetry pattern to other AI-powered commands (, , , , , , ).
This establishes the foundation for tracking AI usage across the application.
This commit introduces several improvements and refactorings across MCP tools, core logic, and configuration.
**Major Changes:**
1. **Refactor updateSubtaskById:**
- Switched from generateTextService to generateObjectService for structured AI responses, using a Zod schema (subtaskSchema) for validation.
- Revised prompts to have the AI generate relevant content based on user request and context (parent/sibling tasks), while explicitly preventing AI from handling timestamp/tag formatting.
- Implemented **local timestamp generation (new Date().toISOString()) and formatting** (using <info added on ...> tags) within the function *after* receiving the AI response. This ensures reliable and correctly formatted details are appended.
- Corrected logic to append only the locally formatted, AI-generated content block to the existing subtask.details.
2. **Consolidate MCP Utilities:**
- Moved/consolidated the withNormalizedProjectRoot HOF into mcp-server/src/tools/utils.js.
- Updated MCP tools (like update-subtask.js) to import withNormalizedProjectRoot from the new location.
3. **Refactor Project Initialization:**
- Deleted the redundant mcp-server/src/core/direct-functions/initialize-project-direct.js file.
- Updated mcp-server/src/core/task-master-core.js to import initializeProjectDirect from its correct location (./direct-functions/initialize-project.js).
**Other Changes:**
- Updated .taskmasterconfig fallback model to claude-3-7-sonnet-20250219.
- Clarified model cost representation in the models tool description (taskmaster.mdc and mcp-server/src/tools/models.js).
Problem: The MCP tool previously handled project root acquisition and path resolution within its method, leading to potential inconsistencies and repetition.
Solution: Refactored the tool () to utilize the new Higher-Order Function (HOF) from .
Specific Changes:
- Imported HOF.
- Updated the Zod schema for the parameter to be optional, as the HOF handles deriving it from the session if not provided.
- Wrapped the entire function body with the HOF.
- Removed the manual call to from within the function body.
- Destructured the from the object received by the wrapped function, ensuring it's the normalized path provided by the HOF.
- Used the normalized variable when calling and when passing arguments to .
This change standardizes project root handling for the tool, simplifies its method, and ensures consistent path normalization. This serves as the pattern for refactoring other MCP tools.
Modified update-subtask-by-id core, direct function, and tool to pass projectRoot for .env API key fallback. Removed check preventing appending details to completed subtasks.
Problem:
- Task Master model configuration wasn't properly checking for API keys in the project's .env file when running through MCP
- The isApiKeySet function was only checking session.env and process.env but not inspecting the .env file directly
- This caused incorrect API key status reporting in MCP tools even when keys were properly set in .env
Solution:
- Modified resolveEnvVariable function in utils.js to properly read from .env file at projectRoot
- Updated isApiKeySet to correctly pass projectRoot to resolveEnvVariable
- Enhanced the key detection logic to have consistent behavior between CLI and MCP contexts
- Maintains the correct precedence: session.env → .env file → process.env
Testing:
- Verified working correctly with both MCP and CLI tools
- API keys properly detected in .env file in both contexts
- Deleted .cursor/mcp.json to confirm introspection of .env as fallback works
- Refactors the core `removeTask` function (`task-manager/remove-task.js`) to accept and iterate over comma-separated task/subtask IDs.
- Updates dependency cleanup and file regeneration logic to run once after processing all specified IDs.
- Adjusts the `remove-task` CLI command (`commands.js`) description and confirmation prompt to handle multiple IDs correctly.
- Fixes a bug in the CLI confirmation prompt where task/subtask titles were not being displayed correctly.
- Updates the `remove_task` MCP tool description to reflect the new multi-ID capability.
This addresses the previously known issue where only the first ID in a comma-separated list was processed.
Closes#140
