Skip to main content
Glama

aida_task_start

Start tracking a new development task or feature implementation to capture structured coding session data for AI development observability.

Instructions

当你开始一个新任务或功能开发时调用。在接到用户需求、开始编码前调用。每个任务的完整数据采集流程:1) aida_task_start 2) 编码 3) aida_log_files 4) aida_log_review 5) aida_task_done。多个子任务必须每个都单独 start/done。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes任务标题,简洁描述要做什么
stageNo所属模块或阶段,如 Authentication, UI, API 等

Implementation Reference

  • Handler function for aida_task_start which initializes a task item and updates the state.
    function handleTaskStart(args: any): any {
      const { path, data } = ensureRunJson();
      const id = nextId(data.tasks, 'TASK');
      const task: TaskItem = {
        taskId: id,
        title: args.title,
        status: 'in-progress',
        stageName: args.stage || 'default',
        prdPhase: '',
        acceptance: '',
        createdAt: now(),
        startedAt: now(),
        completedAt: null,
      };
      data.tasks.push(task);
      data.summary.totalTasks = data.tasks.length;
      data.context.currentTaskId = id;
      addEvent(data, 'task_created', { taskId: id });
      addEvent(data, 'task_started', { taskId: id });
      addTimeline(data, 'task', `${id}: ${args.title}`);
      save(path, data);
  • Tool registration/routing for aida_task_start within the MCP server's message handler.
    case 'aida_task_start':
      result = handleTaskStart(args);
      break;

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/LWTlong/ai-dev-analytics'

If you have feedback or need assistance with the MCP directory API, please join our Discord server