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Get Sub-Agent Job Status

get_sub_agent_job_status

Check the status of a delegated sub-agent job using its job ID. Returns queued, running, done, or error states, with results when complete.

Instructions

Poll a sub-agent job started with start_sub_agent_job. Returns { job_id, status: queued|running|done|error, result? }; result is present for done (shaped like run_sub_agent's response) and error (structured { code, message, retryable }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.5/5.0
Behavior4/5

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 that the tool is for polling (implying a non-destructive read operation) and describes the return structure, including statuses and the presence of result for terminal states. This gives the agent enough to understand the expected behavior without additional annotation support.

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 concise, using one sentence to state the tool's purpose and return format. It front-loads the critical action (poll) and resource (sub-agent job), then immediately provides the response structure. Every word is informative with no redundancy, making it highly efficient.

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 simple polling tool with one parameter and no output schema, the description is complete. It covers the when-to-use (referencing start_sub_agent_job), the return format, and the possible statusesahanincluding result shape for done and error cases. An agent can correctly invoke this tool without needing additional information.

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

Parameters3/5

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

The schema has 0% description coverage, so the description must compensate. The description does not explicitly explain the job_id parameter beyond referencing a job started by start_sub_agent_job, which implies the job_id value. However, the schema already specifies the parameter name and type, so the description adds some context but not detailed semantics. A baseline of 3 is appropriate given the single parameter and the inferred meaning.

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 clearly states the tool's purpose: polling a sub-agent job. It specifies the exact resource (sub-agent job) and the action (poll status), and it distinguishes itself from the sibling tool start_sub_agent_job by referencing it directly. The description also details the return structure, making it unambiguous what the tool does.

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

Usage Guidelines5/5

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

The description explicitly says when to use this tool: to poll a job started with start_sub_agent_job. It does not list alternatives, but given the context of sibling tools, start_sub_agent_job is the only related one, and the description's reference to it provides clear usage context. There are no exclusions, but the usage direction is precise.

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