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get_task_status

Retrieve the current status and structured result for a delegated task by task ID, showing lifecycle state, engine outcome, and cost details.

Instructions

Return status and result for a task started with start_task.

status is the task lifecycle (QUEUED | RUNNING | DONE | FAILED | CANCELLED) — DONE here means "finished", not "succeeded"; check result_data.status / result_data.ok for the engine outcome. result is the pretty-text report; result_data is the structured engine result (status, failure_kind, ok, files_touched, exec_ran, rollback_verified, attempts[], cost_usd, ...).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden. It explains the task lifecycle values, clarifies the subtle meaning of DONE as 'finished' rather than 'succeeded', and details the contents of result_data. This is strong transparency beyond the bare return-type information.

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 compact and well-structured. It leads with the primary purpose, then uses a concise bullet-like list to explain status and result fields. Every sentence adds useful information without redundancy.

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

Completeness5/5

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

For a status-retrieval tool with one parameter and an output schema, the description is complete. It covers the lifecycle semantics, the distinction between textual and structured results, and key result_data fields, so an agent has everything needed to interpret the response correctly.

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 schema provides only task_id with no description, so the description must compensate. It does so by linking task_id to a task started with start_task, giving the parameter practical meaning. It could explicitly describe task_id format or source, but for a single parameter this is sufficient.

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

Purpose5/5

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

The description states a specific verb and resource: 'Return status and result for a task started with start_task.' It clearly distinguishes the tool from the sibling start_task by focusing on retrieval of task outcomes, and it enumerates the exact fields returned.

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

Usage Guidelines4/5

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

The description gives a clear context for use: it is for tasks previously started with start_task. It does not explicitly name alternative tools or exclusion cases, but the association with start_task makes the intended usage unambiguous.

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