diff --git a/mcp-server/src/core/direct-functions/parse-prd.js b/mcp-server/src/core/direct-functions/parse-prd.js index 90417fe4..5f98a165 100644 --- a/mcp-server/src/core/direct-functions/parse-prd.js +++ b/mcp-server/src/core/direct-functions/parse-prd.js @@ -131,21 +131,28 @@ export async function parsePRDDirect(args, log, context = {}) { inputPath, outputPath, numTasks, - { session, mcpLog: logWrapper, projectRoot, useForce, useAppend }, + { + session, + mcpLog: logWrapper, + projectRoot, + useForce, + useAppend, + commandName: 'parse-prd', + outputType: 'mcp' + }, 'json' ); - // parsePRD returns { success: true, tasks: processedTasks } on success - if (result && result.success && Array.isArray(result.tasks)) { - logWrapper.success( - `Successfully parsed PRD. Generated ${result.tasks.length} tasks.` - ); + // Adjust check for the new return structure + if (result && result.success) { + const successMsg = `Successfully parsed PRD and generated tasks in ${result.tasksPath}`; + logWrapper.success(successMsg); return { success: true, data: { - message: `Successfully parsed PRD and generated ${result.tasks.length} tasks.`, - outputPath: outputPath, - taskCount: result.tasks.length + message: successMsg, + outputPath: result.tasksPath, + telemetryData: result.telemetryData } }; } else { diff --git a/scripts/modules/task-manager/parse-prd.js b/scripts/modules/task-manager/parse-prd.js index a5197943..70d5d78f 100644 --- a/scripts/modules/task-manager/parse-prd.js +++ b/scripts/modules/task-manager/parse-prd.js @@ -17,6 +17,7 @@ import { import { generateObjectService } from '../ai-services-unified.js'; import { getDebugFlag } from '../config-manager.js'; import generateTaskFiles from './generate-task-files.js'; +import { displayAiUsageSummary } from '../ui.js'; // Define the Zod schema for a SINGLE task object const prdSingleTaskSchema = z.object({ @@ -95,6 +96,7 @@ async function parsePRD(prdPath, tasksPath, numTasks, options = {}) { let existingTasks = []; let nextId = 1; + let aiServiceResponse = null; try { // Handle file existence and overwrite/append logic @@ -206,8 +208,8 @@ Guidelines: // Call the unified AI service report('Calling AI service to generate tasks from PRD...', 'info'); - // Call generateObjectService with the CORRECT schema - const generatedData = await generateObjectService({ + // Call generateObjectService with the CORRECT schema and additional telemetry params + aiServiceResponse = await generateObjectService({ role: 'main', session: session, projectRoot: projectRoot, @@ -215,7 +217,8 @@ Guidelines: objectName: 'tasks_data', systemPrompt: systemPrompt, prompt: userPrompt, - reportProgress + commandName: 'parse-prd', + outputType: isMCP ? 'mcp' : 'cli' }); // Create the directory if it doesn't exist @@ -223,12 +226,12 @@ Guidelines: if (!fs.existsSync(tasksDir)) { fs.mkdirSync(tasksDir, { recursive: true }); } - logFn.success('Successfully parsed PRD via AI service.'); // Assumes generateObjectService validated + logFn.success('Successfully parsed PRD via AI service.'); // Validate and Process Tasks + const generatedData = aiServiceResponse?.mainResult?.object; + if (!generatedData || !Array.isArray(generatedData.tasks)) { - // This error *shouldn't* happen if generateObjectService enforced prdResponseSchema - // But keep it as a safeguard logFn.error( `Internal Error: generateObjectService returned unexpected data structure: ${JSON.stringify(generatedData)}` ); @@ -265,36 +268,27 @@ Guidelines: ); }); - const allTasks = useAppend + const finalTasks = useAppend ? [...existingTasks, ...processedNewTasks] : processedNewTasks; + const outputData = { tasks: finalTasks }; - const finalTaskData = { tasks: allTasks }; // Use the combined list - - // Write the tasks to the file - writeJSON(tasksPath, finalTaskData); + // Write the final tasks to the file + writeJSON(tasksPath, outputData); report( - `Successfully wrote ${allTasks.length} total tasks to ${tasksPath} (${processedNewTasks.length} new).`, + `Successfully ${useAppend ? 'appended' : 'generated'} ${processedNewTasks.length} tasks in ${tasksPath}`, 'success' ); - report(`Tasks saved to: ${tasksPath}`, 'info'); - // Generate individual task files - if (reportProgress && mcpLog) { - // Enable silent mode when being called from MCP server - enableSilentMode(); - await generateTaskFiles(tasksPath, path.dirname(tasksPath)); - disableSilentMode(); - } else { - await generateTaskFiles(tasksPath, path.dirname(tasksPath)); - } + // Generate markdown task files after writing tasks.json + await generateTaskFiles(tasksPath, path.dirname(tasksPath), { mcpLog }); - // Only show success boxes for text output (CLI) + // Handle CLI output (e.g., success message) if (outputFormat === 'text') { console.log( boxen( chalk.green( - `Successfully generated ${processedNewTasks.length} new tasks. Total tasks in ${tasksPath}: ${allTasks.length}` + `Successfully generated ${processedNewTasks.length} new tasks. Total tasks in ${tasksPath}: ${finalTasks.length}` ), { padding: 1, borderColor: 'green', borderStyle: 'round' } ) @@ -314,9 +308,18 @@ Guidelines: } ) ); + + if (aiServiceResponse && aiServiceResponse.telemetryData) { + displayAiUsageSummary(aiServiceResponse.telemetryData, 'cli'); + } } - return { success: true, tasks: processedNewTasks }; + // Return telemetry data + return { + success: true, + tasksPath, + telemetryData: aiServiceResponse?.telemetryData + }; } catch (error) { report(`Error parsing PRD: ${error.message}`, 'error'); diff --git a/tasks/task_077.txt b/tasks/task_077.txt index 03570941..59ff81e8 100644 --- a/tasks/task_077.txt +++ b/tasks/task_077.txt @@ -24,7 +24,7 @@ # Subtasks: -## 1. Implement telemetry utility and data collection [in-progress] +## 1. Implement telemetry utility and data collection [done] ### Dependencies: None ### Description: Create the logAiUsage utility in ai-services.js that captures all required telemetry data fields ### Details: @@ -96,7 +96,7 @@ Implement HTTPS POST request functionality to securely send the telemetry payloa ### Details: Design and implement a clear privacy notice explaining what data is collected and how it's used. Create a user consent prompt during initial installation/setup that requires explicit opt-in. Store the consent status in the .taskmasterconfig file and respect this setting throughout the application. -## 4. Integrate telemetry into Taskmaster commands [in-progress] +## 4. Integrate telemetry into Taskmaster commands [done] ### Dependencies: 77.1, 77.3 ### Description: Integrate the telemetry utility across all relevant Taskmaster commands ### Details: @@ -107,13 +107,13 @@ Modify each Taskmaster command (expand, parse-prd, research, etc.) to call the l Successfully integrated telemetry calls into `addTask` (core) and `addTaskDirect` (MCP) functions by passing `commandName` and `outputType` parameters to the telemetry system. The `ai-services-unified.js` module now logs basic telemetry data, including calculated cost information, whenever the `add-task` command or tool is invoked. This integration respects user consent settings and maintains performance standards. -## 5. Implement usage summary display [pending] +## 5. Implement usage summary display [done] ### Dependencies: 77.1, 77.4 ### Description: Create an optional feature to display AI usage summary in the CLI output ### Details: Develop functionality to display a concise summary of AI usage (tokens used, estimated cost) directly in the CLI output after command execution. Make this feature configurable through Taskmaster settings. Ensure the display is formatted clearly and doesn't clutter the main command output. -## 6. Telemetry Integration for parse-prd [pending] +## 6. Telemetry Integration for parse-prd [done] ### Dependencies: None ### Description: Integrate AI usage telemetry capture and propagation for the parse-prd functionality. ### Details: @@ -136,7 +136,7 @@ Apply telemetry pattern from telemetry.mdc: * Verify `handleApiResult` correctly passes `data.telemetryData` through. -## 7. Telemetry Integration for expand-task [pending] +## 7. Telemetry Integration for expand-task [in-progress] ### Dependencies: None ### Description: Integrate AI usage telemetry capture and propagation for the expand-task functionality. ### Details: diff --git a/tasks/tasks.json b/tasks/tasks.json index 3af44bbb..4cf5048c 100644 --- a/tasks/tasks.json +++ b/tasks/tasks.json @@ -4017,7 +4017,7 @@ "description": "Create the logAiUsage utility in ai-services.js that captures all required telemetry data fields", "dependencies": [], "details": "Develop the logAiUsage function that collects timestamp, userId, commandName, modelUsed, inputTokens, outputTokens, totalTokens, and totalCost. Implement token counting logic and cost calculation using pricing from supported_models.json. Ensure proper error handling and data validation.\n\nDevelop the logAiUsage function that collects timestamp, userId, commandName, modelUsed, inputTokens, outputTokens, totalTokens, and totalCost. Implement token counting logic and cost calculation using pricing from supported_models.json. Ensure proper error handling and data validation.\n\nImplementation Plan:\n1. Define `logAiUsage` function in `ai-services-unified.js` that accepts parameters: userId, commandName, providerName, modelId, inputTokens, and outputTokens.\n\n2. Implement data collection and calculation logic:\n - Generate timestamp using `new Date().toISOString()`\n - Calculate totalTokens by adding inputTokens and outputTokens\n - Create a helper function `_getCostForModel(providerName, modelId)` that:\n - Loads pricing data from supported-models.json\n - Finds the appropriate provider/model entry\n - Returns inputCost and outputCost rates or defaults if not found\n - Calculate totalCost using the formula: ((inputTokens/1,000,000) * inputCost) + ((outputTokens/1,000,000) * outputCost)\n - Assemble complete telemetryData object with all required fields\n\n3. Add initial logging functionality:\n - Use existing log utility to record telemetry data at 'info' level\n - Implement proper error handling with try/catch blocks\n\n4. Integrate with `_unifiedServiceRunner`:\n - Modify to accept commandName and userId parameters\n - After successful API calls, extract usage data from results\n - Call logAiUsage with the appropriate parameters\n\n5. Update provider functions in src/ai-providers/*.js:\n - Ensure all provider functions return both the primary result and usage statistics\n - Standardize the return format to include a usage object with inputTokens and outputTokens\n\n\nTo implement the AI usage telemetry effectively, we need to update each command across our different stacks. Let's create a structured approach for this implementation:\n\nCommand Integration Plan:\n1. Core Function Commands:\n - Identify all AI-utilizing commands in the core function library\n - For each command, modify to pass commandName and userId to _unifiedServiceRunner\n - Update return handling to process and forward usage statistics\n\n2. Direct Function Commands:\n - Map all direct function commands that leverage AI capabilities\n - Implement telemetry collection at the appropriate execution points\n - Ensure consistent error handling and telemetry reporting\n\n3. MCP Tool Stack Commands:\n - Inventory all MCP commands with AI dependencies\n - Standardize the telemetry collection approach across the tool stack\n - Add telemetry hooks that maintain backward compatibility\n\nFor each command category, we'll need to:\n- Document current implementation details\n- Define specific code changes required\n- Create tests to verify telemetry is being properly collected\n- Establish validation procedures to ensure data accuracy\n", - "status": "in-progress", + "status": "done", "testStrategy": "Unit test the utility with mock AI usage data to verify all fields are correctly captured and calculated" }, { @@ -4049,7 +4049,7 @@ 3 ], "details": "Modify each Taskmaster command (expand, parse-prd, research, etc.) to call the logAiUsage utility after AI interactions. Ensure telemetry is only sent if user has provided consent. Implement the integration in a way that doesn't impact command performance or user experience.\n\nModify each Taskmaster command (expand, parse-prd, research, etc.) to call the logAiUsage utility after AI interactions. Ensure telemetry is only sent if user has provided consent. Implement the integration in a way that doesn't impact command performance or user experience.\n\nSuccessfully integrated telemetry calls into `addTask` (core) and `addTaskDirect` (MCP) functions by passing `commandName` and `outputType` parameters to the telemetry system. The `ai-services-unified.js` module now logs basic telemetry data, including calculated cost information, whenever the `add-task` command or tool is invoked. This integration respects user consent settings and maintains performance standards.\n", - "status": "in-progress", + "status": "done", "testStrategy": "Integration tests to verify telemetry is correctly triggered across different commands with proper data" }, { @@ -4061,7 +4061,7 @@ 4 ], "details": "Develop functionality to display a concise summary of AI usage (tokens used, estimated cost) directly in the CLI output after command execution. Make this feature configurable through Taskmaster settings. Ensure the display is formatted clearly and doesn't clutter the main command output.", - "status": "pending", + "status": "done", "testStrategy": "User acceptance testing to verify the summary display is clear, accurate, and properly configurable" }, { @@ -4069,7 +4069,7 @@ "title": "Telemetry Integration for parse-prd", "description": "Integrate AI usage telemetry capture and propagation for the parse-prd functionality.", "details": "\\\nApply telemetry pattern from telemetry.mdc:\n\n1. **Core (`scripts/modules/task-manager/parse-prd.js`):**\n * Modify AI service call to include `commandName: \\'parse-prd\\'` and `outputType`.\n * Receive `{ mainResult, telemetryData }`.\n * Return object including `telemetryData`.\n * Handle CLI display via `displayAiUsageSummary` if applicable.\n\n2. **Direct (`mcp-server/src/core/direct-functions/parse-prd.js`):**\n * Pass `commandName`, `outputType: \\'mcp\\'` to core.\n * Pass `outputFormat: \\'json\\'` if applicable.\n * Receive `{ ..., telemetryData }` from core.\n * Return `{ success: true, data: { ..., telemetryData } }`.\n\n3. **Tool (`mcp-server/src/tools/parse-prd.js`):**\n * Verify `handleApiResult` correctly passes `data.telemetryData` through.\n", - "status": "pending", + "status": "done", "dependencies": [], "parentTaskId": 77 }, @@ -4078,7 +4078,7 @@ "title": "Telemetry Integration for expand-task", "description": "Integrate AI usage telemetry capture and propagation for the expand-task functionality.", "details": "\\\nApply telemetry pattern from telemetry.mdc:\n\n1. **Core (`scripts/modules/task-manager/expand-task.js`):**\n * Modify AI service call to include `commandName: \\'expand-task\\'` and `outputType`.\n * Receive `{ mainResult, telemetryData }`.\n * Return object including `telemetryData`.\n * Handle CLI display via `displayAiUsageSummary` if applicable.\n\n2. **Direct (`mcp-server/src/core/direct-functions/expand-task.js`):**\n * Pass `commandName`, `outputType: \\'mcp\\'` to core.\n * Pass `outputFormat: \\'json\\'` if applicable.\n * Receive `{ ..., telemetryData }` from core.\n * Return `{ success: true, data: { ..., telemetryData } }`.\n\n3. **Tool (`mcp-server/src/tools/expand-task.js`):**\n * Verify `handleApiResult` correctly passes `data.telemetryData` through.\n", - "status": "pending", + "status": "in-progress", "dependencies": [], "parentTaskId": 77 },