api

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

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds useful context: 'Free — no credits consumed' and explains what happens at different statuses (complete/failed includes bundle_id or result_json). No contradictions.

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 well-structured: purpose, usage, return format, and example prompts. Every sentence adds value without redundancy.

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 output schema is provided within the description, it covers all return fields. It also explains job creation sources and how to use the tool in a polling loop. For a simple polling tool, this is complete.

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% but the description adds context: 'Job ID (job_...) returned by async extract or collection.add_document. Example: job_550e8400-...' which clarifies the parameter's origin and format beyond the schema.

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 'Poll the status of an async job (extract, indexing, batch)' with a specific verb and resource. It distinguishes from sibling tools like collection.add_document or url.extract by focusing on status polling.

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 says when to use: 'Use after collection.add_document or async extract to check when processing completes.' It also provides guidance on polling loop: 'Poll this endpoint in a loop until status is complete or failed.' It does not explicitly state when not to use, but the context is clear.

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

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, with no overlap. The category prefixes (account, bundle, collection, document, job, receipt, url) and specific action names (get, notarize, verify, create, list, etc.) ensure that an agent can unambiguously select the correct tool for any task.

Naming Consistency5/5

All tools follow a consistent category.action or category.action_noun pattern using snake_case (e.g., bundle.get, collection.add_document, url.translate). No mixed conventions or irregular names, making the pattern predictable and easy to learn.

Tool Count4/5

With 22 tools, the set is somewhat large but each tool addresses a distinct need within a broad domain (evidence management, document AI, collections, URL processing, job tracking, receipts). The count is slightly above the typical well-scoped range but still reasonable given the scope.

Completeness2/5

The tool surface has notable gaps: there is no tool to create or delete an evidence bundle, nor to update collections or bundles. The core workflow of creating a bundle from a document is missing, and the lifecycle is incomplete, which would likely cause agent failures in typical use cases.

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