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Task Manager MCP Server

by blizzy78

Get current task

current_task

Retrieve tasks currently in progress to monitor ongoing work and track task status within the Task Manager MCP Server.

Instructions

Returns a list of tasks that are currently in progress.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'current_task' tool. It retrieves the current task from TaskDB, gets all tasks in its tree and incomplete tasks, formats them, and returns structured content with a note to use task_info for details.
    export async function handleCurrentTask(_: CurrentTaskArgs, taskDB: TaskDB) {
      const currentTaskID = taskDB.getCurrentTask()
      if (!currentTaskID) {
        const res = { tasks: [] }
    
        return {
          content: [],
          structuredContent: res,
        } satisfies CallToolResult
      }
    
      const tasksInTree = taskDB.getAllInTree(currentTaskID)
      const incompleteTaskIDs = taskDB.incompleteTasksInTree(currentTaskID).map((t) => t.taskID)
    
      const res = {
        tasks: tasksInTree.map((t) =>
          toBasicTaskInfo(t, true, t.status !== DoneStatus && t.status !== FailedStatus, t.status === TodoStatus)
        ),
        incompleteTasksIdealOrder: incompleteTaskIDs,
      }
    
      return {
        content: [
          {
            type: 'text',
            text: "Use 'task_info' tool to retrieve full task details",
            audience: ['assistant'],
          } satisfies TextContent,
        ],
    
        structuredContent: res,
      } satisfies CallToolResult
    }
  • Zod schema definition for the input arguments of the current_task tool, which takes no parameters.
    export const CurrentTaskArgsSchema = z.object({})
    
    type CurrentTaskArgs = z.infer<typeof CurrentTaskArgsSchema>
  • Tool definition object with name, title, description, and JSON schema for input, used for MCP tool registration.
    export const CURRENT_TASK = 'current_task'
    
    export const currentTaskTool = {
      name: CURRENT_TASK,
      title: 'Get current task',
      description: 'Returns a list of tasks that are currently in progress.',
      inputSchema: zodToJsonSchema(CurrentTaskArgsSchema, { $refStrategy: 'none' }),
    }
  • tools/index.ts:44-47 (registration)
    Registers the 'current_task' tool handler and schema in the central toolHandlers map.
    [CURRENT_TASK]: {
      handler: handleCurrentTask,
      schema: CurrentTaskArgsSchema,
    } satisfies ToolHandlerInfo,
  • TaskDB helper method to retrieve the ID of the current task, directly called by the handler.
    getCurrentTask(): TaskID | null {
      return this.currentTaskID
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a list of in-progress tasks, which implies a read-only operation, but doesn't cover aspects like authentication needs, rate limits, error handling, or the format of the returned list. For a tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that directly states the tool's function without any unnecessary words. It is front-loaded with the core action ('Returns'), making it efficient and easy to parse. Every part of the sentence earns its place by conveying essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimal. It states what the tool does but lacks context on usage, behavioral traits, or output details. For a tool that returns a list, without an output schema, the description should ideally hint at the return format or structure, but it doesn't, leaving gaps in completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied for zero parameters, as the schema fully handles the parameter semantics without requiring description input.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Returns') and resource ('list of tasks that are currently in progress'), making the purpose evident. It distinguishes from siblings like 'create_task' (creation) and 'update_task' (modification) by focusing on retrieval of in-progress tasks. However, it doesn't explicitly differentiate from 'task_info', which might also retrieve task details, leaving some ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'current_task' over 'task_info' (which might get task details) or other siblings, nor does it specify prerequisites or contexts for usage. This lack of explicit when/when-not instructions limits its utility for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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