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velrim_job_status

Read-onlyIdempotent

Poll an asynchronous extraction job by its job_id (from POST /v1/jobs on the Velrim API; velrim_extract itself is synchronous and never creates jobs). Returns the job status and, once complete, the same typed result as velrim_extract: per field a state (present, null, or missing), a calibrated confidence score, and an anchor (source page and bounding box). Confidence is calibrated against published reliability curves (https://velrim.com/reliability) — branch the same way: act on a field at or above your accept threshold, escalate the rest, and use each anchor to check a field against its source page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesThe job_id returned by an asynchronous Velrim extraction job (created via the Velrim API or an SDK).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
job_idNo
resultNo
statusNo"running" | "succeeded" | "failed" (additive-tolerant).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, openWorld), and the description adds substantive behavior beyond them: the polling lifecycle, that the result only appears once the job completes, the per-field result shape (state/confidence/anchor), and how calibration curves should drive accept-vs-escalate branching. This is unusually rich operational context.

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 polling action and the sibling distinction, which is exactly what an agent needs first. The final sentence on reliability curves and branching is longer and advisory, but it still carries actionable decision guidance rather than 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?

An output schema exists, yet the description still summarizes the return contract (state, confidence, anchor) and explains how to act on it. For a single-parameter polling tool, 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 coverage is 100% for the single job_id parameter, so the baseline is 3. The description adds origin and traceability context ('from POST /v1/jobs', 'velrim_extract itself is synchronous and never creates jobs') that helps an agent source a valid value rather than guess.

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 opens with a specific verb and resource ('Poll an asynchronous extraction job by its job_id') and immediately contrasts itself with the sibling tool by noting velrim_extract is synchronous and never creates jobs. An agent can route between the two without opening either schema.

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?

It explicitly states the condition that selects this tool (a job_id from POST /v1/jobs, or a job created via the Velrim API/SDK) and names the alternative plus why that alternative is not applicable. The when-to-use boundary is unambiguous.

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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