fix(commands): implement manual creation mode for add-task command
- Add support for --title/-t and --description/-d flags in add-task command - Fix validation for manual creation mode (title + description) - Implement proper testing for both prompt and manual creation modes - Update testing documentation with Commander.js testing best practices - Add guidance on handling variable hoisting and module initialization issues Changeset: brave-doors-open.md
This commit is contained in:
Eyal Toledano
@@ -23,12 +23,16 @@ import {
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* Direct function wrapper for adding a new task with error handling.
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*
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* @param {Object} args - Command arguments
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* @param {string} args.prompt - Description of the task to add
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* @param {Array<number>} [args.dependencies=[]] - Task dependencies as array of IDs
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* @param {string} [args.prompt] - Description of the task to add (required if not using manual fields)
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* @param {string} [args.title] - Task title (for manual task creation)
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* @param {string} [args.description] - Task description (for manual task creation)
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* @param {string} [args.details] - Implementation details (for manual task creation)
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* @param {string} [args.testStrategy] - Test strategy (for manual task creation)
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* @param {string} [args.dependencies] - Comma-separated list of task IDs this task depends on
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* @param {string} [args.priority='medium'] - Task priority (high, medium, low)
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* @param {string} [args.file] - Path to the tasks file
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* @param {string} [args.file='tasks/tasks.json'] - Path to the tasks file
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* @param {string} [args.projectRoot] - Project root directory
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* @param {boolean} [args.research] - Whether to use research capabilities for task creation
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* @param {boolean} [args.research=false] - Whether to use research capabilities for task creation
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* @param {Object} log - Logger object
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* @param {Object} context - Additional context (reportProgress, session)
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* @returns {Promise<Object>} - Result object { success: boolean, data?: any, error?: { code: string, message: string } }
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@@ -41,15 +45,18 @@ export async function addTaskDirect(args, log, context = {}) {
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// Find the tasks.json path
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const tasksPath = findTasksJsonPath(args, log);
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// Check if this is manual task creation or AI-driven task creation
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const isManualCreation = args.title && args.description;
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// Check required parameters
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if (!args.prompt) {
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log.error('Missing required parameter: prompt');
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if (!args.prompt && !isManualCreation) {
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log.error('Missing required parameters: either prompt or title+description must be provided');
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disableSilentMode();
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return {
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success: false,
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error: {
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code: 'MISSING_PARAMETER',
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message: 'The prompt parameter is required for adding a task'
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message: 'Either the prompt parameter or both title and description parameters are required for adding a task'
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}
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};
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}
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@@ -65,120 +72,160 @@ export async function addTaskDirect(args, log, context = {}) {
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: [];
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const priority = args.priority || 'medium';
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log.info(
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`Adding new task with prompt: "${prompt}", dependencies: [${dependencies.join(', ')}], priority: ${priority}`
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);
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// Extract context parameters for advanced functionality
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// Commenting out reportProgress extraction
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// const { reportProgress, session } = context;
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const { session } = context; // Keep session
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const { session } = context;
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// Initialize AI client with session environment
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let localAnthropic;
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try {
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localAnthropic = getAnthropicClientForMCP(session, log);
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} catch (error) {
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log.error(`Failed to initialize Anthropic client: ${error.message}`);
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disableSilentMode();
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return {
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success: false,
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error: {
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code: 'AI_CLIENT_ERROR',
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message: `Cannot initialize AI client: ${error.message}`
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}
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let manualTaskData = null;
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if (isManualCreation) {
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// Create manual task data object
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manualTaskData = {
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title: args.title,
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description: args.description,
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details: args.details || '',
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testStrategy: args.testStrategy || ''
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};
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}
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// Get model configuration from session
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const modelConfig = getModelConfig(session);
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// Read existing tasks to provide context
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let tasksData;
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try {
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const fs = await import('fs');
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tasksData = JSON.parse(fs.readFileSync(tasksPath, 'utf8'));
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} catch (error) {
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log.warn(`Could not read existing tasks for context: ${error.message}`);
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tasksData = { tasks: [] };
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}
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// Build prompts for AI
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const { systemPrompt, userPrompt } = _buildAddTaskPrompt(
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prompt,
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tasksData.tasks
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);
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// Make the AI call using the streaming helper
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let responseText;
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try {
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responseText = await _handleAnthropicStream(
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localAnthropic,
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{
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model: modelConfig.model,
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max_tokens: modelConfig.maxTokens,
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temperature: modelConfig.temperature,
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messages: [{ role: 'user', content: userPrompt }],
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system: systemPrompt
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},
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{
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// reportProgress: context.reportProgress, // Commented out to prevent Cursor stroking out
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mcpLog: log
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}
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log.info(
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`Adding new task manually with title: "${args.title}", dependencies: [${dependencies.join(', ')}], priority: ${priority}`
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);
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} catch (error) {
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log.error(`AI processing failed: ${error.message}`);
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// Call the addTask function with manual task data
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const newTaskId = await addTask(
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tasksPath,
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null, // No prompt needed for manual creation
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dependencies,
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priority,
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{
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mcpLog: log,
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session
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},
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'json', // Use JSON output format to prevent console output
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null, // No custom environment
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manualTaskData // Pass the manual task data
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);
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// Restore normal logging
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disableSilentMode();
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return {
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success: false,
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error: {
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code: 'AI_PROCESSING_ERROR',
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message: `Failed to generate task with AI: ${error.message}`
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success: true,
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data: {
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taskId: newTaskId,
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message: `Successfully added new task #${newTaskId}`
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}
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};
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}
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} else {
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// AI-driven task creation
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log.info(
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`Adding new task with prompt: "${prompt}", dependencies: [${dependencies.join(', ')}], priority: ${priority}`
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);
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// Parse the AI response
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let taskDataFromAI;
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try {
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taskDataFromAI = parseTaskJsonResponse(responseText);
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} catch (error) {
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log.error(`Failed to parse AI response: ${error.message}`);
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disableSilentMode();
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return {
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success: false,
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error: {
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code: 'RESPONSE_PARSING_ERROR',
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message: `Failed to parse AI response: ${error.message}`
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}
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};
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}
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// Call the addTask function with 'json' outputFormat to prevent console output when called via MCP
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const newTaskId = await addTask(
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tasksPath,
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prompt,
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dependencies,
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priority,
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{
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// reportProgress, // Commented out
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mcpLog: log,
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session,
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taskDataFromAI // Pass the parsed AI result
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},
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'json'
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);
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// Restore normal logging
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disableSilentMode();
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return {
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success: true,
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data: {
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taskId: newTaskId,
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message: `Successfully added new task #${newTaskId}`
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// Initialize AI client with session environment
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let localAnthropic;
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try {
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localAnthropic = getAnthropicClientForMCP(session, log);
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} catch (error) {
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log.error(`Failed to initialize Anthropic client: ${error.message}`);
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disableSilentMode();
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return {
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success: false,
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error: {
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code: 'AI_CLIENT_ERROR',
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message: `Cannot initialize AI client: ${error.message}`
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}
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};
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}
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};
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// Get model configuration from session
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const modelConfig = getModelConfig(session);
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// Read existing tasks to provide context
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let tasksData;
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try {
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const fs = await import('fs');
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tasksData = JSON.parse(fs.readFileSync(tasksPath, 'utf8'));
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} catch (error) {
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log.warn(`Could not read existing tasks for context: ${error.message}`);
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tasksData = { tasks: [] };
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}
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// Build prompts for AI
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const { systemPrompt, userPrompt } = _buildAddTaskPrompt(
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prompt,
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tasksData.tasks
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);
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// Make the AI call using the streaming helper
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let responseText;
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try {
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responseText = await _handleAnthropicStream(
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localAnthropic,
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{
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model: modelConfig.model,
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max_tokens: modelConfig.maxTokens,
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temperature: modelConfig.temperature,
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messages: [{ role: 'user', content: userPrompt }],
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system: systemPrompt
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},
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{
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mcpLog: log
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}
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);
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} catch (error) {
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log.error(`AI processing failed: ${error.message}`);
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disableSilentMode();
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return {
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success: false,
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error: {
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code: 'AI_PROCESSING_ERROR',
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message: `Failed to generate task with AI: ${error.message}`
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}
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};
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}
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// Parse the AI response
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let taskDataFromAI;
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try {
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taskDataFromAI = parseTaskJsonResponse(responseText);
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} catch (error) {
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log.error(`Failed to parse AI response: ${error.message}`);
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disableSilentMode();
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return {
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success: false,
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error: {
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code: 'RESPONSE_PARSING_ERROR',
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message: `Failed to parse AI response: ${error.message}`
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}
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};
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}
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// Call the addTask function with 'json' outputFormat to prevent console output when called via MCP
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const newTaskId = await addTask(
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tasksPath,
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prompt,
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dependencies,
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priority,
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{
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mcpLog: log,
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session
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},
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'json',
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null,
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taskDataFromAI // Pass the parsed AI result as the manual task data
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);
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// Restore normal logging
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disableSilentMode();
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return {
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success: true,
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data: {
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taskId: newTaskId,
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message: `Successfully added new task #${newTaskId}`
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}
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};
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}
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} catch (error) {
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// Make sure to restore normal logging even if there's an error
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disableSilentMode();
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@@ -22,7 +22,11 @@ export function registerAddTaskTool(server) {
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name: 'add_task',
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description: 'Add a new task using AI',
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parameters: z.object({
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prompt: z.string().describe('Description of the task to add'),
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prompt: z.string().optional().describe('Description of the task to add (required if not using manual fields)'),
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title: z.string().optional().describe('Task title (for manual task creation)'),
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description: z.string().optional().describe('Task description (for manual task creation)'),
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details: z.string().optional().describe('Implementation details (for manual task creation)'),
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testStrategy: z.string().optional().describe('Test strategy (for manual task creation)'),
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dependencies: z
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.string()
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.optional()
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@@ -31,11 +35,11 @@ export function registerAddTaskTool(server) {
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.string()
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.optional()
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.describe('Task priority (high, medium, low)'),
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file: z.string().optional().describe('Absolute path to the tasks file'),
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file: z.string().optional().describe('Path to the tasks file (default: tasks/tasks.json)'),
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projectRoot: z
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.string()
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.optional()
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.describe('Root directory of the project'),
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.describe('Root directory of the project (default: current working directory)'),
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research: z
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.boolean()
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.optional()
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