Entity Enricher

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; answer a delegated job's llm_requests with answer_llm_request. 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. On a delegated job, llm_requests lists the pending model calls: the first complete, the rest as envelopes; llm_request_id= returns that one complete. No LLM call. Polling, failure and recovery guidance: enricher://docs/enrichment-and-fusion.

Input Schema

Name	Required	Description
`job_id`	Yes	Job ID returned by a start tool.
`events_after`	No	Event-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_result`	No	Include the full terminal result payload (can be large). Default returns a compact scalar summary per model.
`llm_request_id`	No	On a job delegating its LLM calls: return this pending request complete (messages, systemPrompt, tools, toolChoice, attachments) as the only entry of llm_requests. request_not_pending when it is no longer waiting.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 22, 2026

addedInput schema / properties / llm_request_id

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "On a job delegating its LLM calls: return this pending request complete (messages, systemPrompt, tools, toolChoice, attachments) as the only entry of llm_requests. request_not_pending when it is no longer waiting.",
+  "title": "Llm Request Id"
+}

First observedSep 25, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Goes well beyond the readOnly/destructive annotations: it discloses that a failed job is not usable output, that unknown IDs may be invalid, expired, or lost after restart, that no LLM call is made, and that event logs are capped at 100 per call with events_has_more signaling. These are the failure and recovery traits an agent cannot infer from annotations.

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

Conciseness4/5

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

Front-loaded with the core purpose and the six status values, then layers routing, failure, and cursor details. It is dense and mostly earns its length, though the long run-on sentences (events_after and llm_request_id clauses) make it read as a wall of text.

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 already documenting returns, this covers the remaining burden: status vocabulary, failure interpretation, restart-loss recovery path, event-log pagination, and a documentation pointer for deeper polling/recovery guidance. Nothing an agent needs to call it correctly is missing.

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 description coverage is 100%, so the baseline is 3; the description adds real semantics on top by framing events_after as the poll equivalent of the SSE stream with the last_seq continuation pattern, and by explaining the relationship between the llm_requests list and llm_request_id. It stops short of full detail on include_result size implications, which the schema already covers.

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?

States a specific verb (read) and resource (job status) plus the exact payload shape: status, progress, compact terminal summary with persisted record IDs. It also enumerates the status vocabulary, so an agent knows precisely what this returns and how it differs from sibling tools like cancel_job or list_records.

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?

Explicitly routes the agent elsewhere for every adjacent case: relay pause questions to answer_job_question, answer delegated llm_requests with answer_llm_request, and use list_records(job_id=...) to recover persisted outputs for unknown IDs. It also distinguishes polling from the SSE stream via the events_after cursor, which is exactly the when-to-use guidance a status tool needs.

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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