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DocImprint Document Intelligence

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Get Job Status

job.status
Read-onlyIdempotent

Retrieve the current status and progress of asynchronous document processing jobs. Poll until the job completes or fails to get results or error details.

Instructions

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
NameRequiredDescriptionDefault
job_idYesJob ID (job_...) returned by async extract or collection.add_document. Example: "job_550e8400-e29b-41d4-a716-446655440000"

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
typeYes
errorYes
statusYes
bundle_idYes
created_atYes
result_jsonYes
completed_atYes
progress_pctYes
collection_idYes
progress_messageYes
Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description adds meaningful behavioral context: it is a polling endpoint, free with no credits consumed, completed jobs include bundle_id or result_json, and it details status values and progress fields. This provides a full picture of how the tool behaves at runtime.

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 moderately sized but well-structured, with a clear explanation, a return format block, and example prompts. Minor redundancy exists (bundle_id mentioned twice), but overall it is organized and each section serves a purpose.

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?

Given the tool's simplicity, the description is thorough: it covers usage context, polling behavior, cost, return structure, and state transitions. The output schema is present, but the description independently explains the fields and statuses, making the tool fully understandable without external references.

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 covers the single parameter (job_id) with a description and example. The description does not add additional parameter-specific semantics beyond the schema, so baseline 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 an async job, listing specific job types (extract, indexing, batch). It distinguishes itself from sibling tools by focusing on job status rather than data extraction or collection management, making its purpose unambiguous.

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 when to use the tool: after collection.add_document or async extract, and to poll in a loop until complete or failed. It also mentions when jobs are created (POST /v1/extract with webhook, or add_document), and provides example prompts, giving clear usage context and no misleading alternatives.

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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