DocImprint

Get Job Status

job.status

Read-onlyIdempotent

Poll the status of an async job (extract, indexing, batch). Free — no credits consumed. Use after collection.add_document or async extract to check when processing completes. Poll this endpoint in a loop until status is "complete" or "failed". Completed jobs include the bundle_id or result_json in the response. Jobs are created when you POST /v1/extract with a webhook, or when collection.add_document triggers async indexing. Returns: { id, type: "extract"|"extract_batch"|"index_collection", status: "queued"|"processing"|"complete"|"failed"|"cancelled", progress_pct: number (0–100), progress_message, bundle_id (when complete), result_json (when complete), error (when failed), created_at, completed_at } Example prompts:

"Check the status of my indexing job job_550e8400."
"Is my async extract job done yet?"
"Poll job [job_id] — what is the current progress?"

Input Schema

TableJSON Schema

Name	Required	Description	Default
`job_id`	Yes	Job ID (job_...) returned by async extract or collection.add_document. Example: "job_550e8400-e29b-41d4-a716-446655440000"

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes
`type`	Yes
`error`	Yes
`status`	Yes
`bundle_id`	Yes
`created_at`	Yes
`result_json`	Yes
`completed_at`	Yes
`progress_pct`	Yes
`collection_id`	Yes
`progress_message`	Yes

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe repeated calls. The description adds valuable behavior details: it's free, jobs are created by specific endpoints, and the polling loop approach. It also describes the response schema including progress_pct and error fields. No contradictions with 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?

The description is well-structured with a clear purpose first, followed by usage, return schema, and example prompts. It is slightly lengthy but each sentence adds value. The inclusion of a full return schema and examples enhances clarity without being wasteful.

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?

The description is complete given the tool's complexity and the presence of a detailed output schema within the description itself. It covers purpose, usage, polling behavior, job creation triggers, and the full response structure. No gaps remain.

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 input schema already describes the job_id parameter with an example and required status. The description repeats the example but does not add new semantic meaning beyond what the schema provides. With 100% schema coverage, a baseline score of 3 is appropriate.

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 polls the status of async jobs (extract, indexing, batch). It specifies the verb 'poll' and the resource 'status of an async job'. It distinguishes itself from sibling tools by being the only status checking endpoint, and provides context on when jobs are created.

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 explicitly states when to use this tool: after collection.add_document or async extract, and recommends polling in a loop until status is 'complete' or 'failed'. It does not mention when not to use or provide alternatives, but since there is no other status tool among siblings, this is acceptable.

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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TDQS

A4.5/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, grouped by domain (account, bundle, collection, document, job, receipt, url). Descriptions and naming make it easy to differentiate between similar tools like url.extract vs document.extract_text or collection.search vs collection.ask.

Naming Consistency5/5

All tools follow the same prefix.group_action pattern in snake_case (e.g., account.quota, bundle.get, collection.create). No mixing of conventions, making the API predictable and easy to navigate.

Tool Count5/5

With 22 tools, the server covers a comprehensive set of operations for document and evidence management. Each tool serves a specific purpose, and the count feels well-scoped without being bloated or sparse.

Completeness3/5

The tool surface lacks explicit create and delete operations for bundles and collections. Bundles appear to be created externally, and there is no tool to remove a bundle or collection. This is a notable gap given the server's stated purpose.

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