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Get job status

get_job_status
Read-only

Read a job's status, progress and compact terminal summary with persisted record IDs. Status is pending, running, paused, completed, failed or cancelled. Relay pause questions with answer_job_question. include_result=true returns full terminal details; batch summaries count entities and database outcomes. A failed job is not usable output; inspect individual records for partial recovery. Unknown IDs may be invalid, expired or lost after restart: look for persisted outputs with list_records(job_id=...), without assuming success. events_after= adds the job's event log past that cursor (per-model completions, scoring progress, pauses) — the poll equivalent of the SSE stream; pass the returned last_seq next time. No LLM call. Polling, failure and recovery guidance: enricher://docs/enrichment-and-fusion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesJob ID returned by a start tool.
events_afterNoEvent-log cursor: 0 returns the job's events from the start, a previous call's last_seq returns only the newer ones (at most 100 per call; events_has_more says when to call again). Omit to skip the log.
include_resultNoInclude the full terminal result payload (can be large). Default returns a compact scalar summary per model.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Despite readOnlyHint and destructiveHint already indicating a safe read, the description adds substantial behavior: possible statuses, include_result behavior, failed-job semantics, unknown ID causes, event log semantics, and 'No LLM call'. It even notes the polling relationship to the SSE stream, which is valuable beyond annotations.

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 dense but each sentence carries operational value: scope, statuses, alternative tools, result modes, failure handling, unknown IDs, event cursor mechanics, and a documentation link. It is front-loaded with the core purpose and avoids filler.

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 polling/status tool with an output schema, the description is complete. It covers all parameters, failure modes, recovery paths, alternative tools, and the polling loop. The presence of an output schema means return-value details are not required, and nothing essential is left out.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining what include_result returns ('full terminal details', 'batch summaries count entities and database outcomes') and how events_after works as a cursor ('pass the returned last_seq next time'). This is more than a restatement.

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 starts with a clear action verb and resource: 'Read a job's status, progress and compact terminal summary'. It also differentiates itself from related tools by naming answer_job_question and list_records as separate paths, making the tool's scope unmistakable.

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 tells the agent when to use alternative tools: 'Relay pause questions with answer_job_question' and for unknown IDs, 'look for persisted outputs with list_records(job_id=...), without assuming success'. It also explains the polling pattern with events_after and points to docs for failure and recovery guidance.

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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