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Get (or wait on) a form detection run

get_form_detection_run
Read-onlyIdempotent

Get the state and output of a form detection run (edit group) — resume a status: "running" run or inspect a failed one. wait: true blocks until terminal or wait-budget expiry (call again to keep waiting). Form detection runs have no list endpoint — keep the run ID. Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoBlock until terminal or budget expiry. Default false.
runIdYesThe run ID returned by detect_form_fields.
environmentYes"TEST" = the Test (development) environment, "PRODUCTION" = live. Must match a granted target from get_me (an API key pins one environment).
waitSecondsNoMax seconds to block waiting on the run (clamped to the server wait budget).
workspaceIdYesTarget workspace (ws_...). Must be a granted workspace — get_me lists the accepted values.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
runIdYes
outputNoDetected edit schema (PROCESSED only).
statusYesPROCESSING | PROCESSED | FAILED, or "running" (resume via the get tool).
metricsNo
runTypeNo
llmContextNo
failureReasonNo
failureMessageNo

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses important runtime behavior beyond the annotations: wait mode blocks until terminal state or budget expiry, repeated calls are needed to continue waiting, and results may include llmContext guidance. This is genuinely useful and not already captured by readOnly/openWorld/idempotent hints.

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 compact and front-loaded: purpose first, then wait behavior, then operational constraints, then output guidance. Every sentence adds actionable information without waste.

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 rich input schema and the existence of an output schema, the description covers the important gaps: it tells the agent that there is no list endpoint, that run IDs must be retained, how waiting works, and that llmContext should be followed. This is complete for safe and correct invocation.

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%, so the baseline is 3. The description adds value by explaining the waiting loop ('call again to keep waiting') and framing wait behavior around terminal states and budgets, which enriches the wait/waitSeconds parameter semantics beyond the schema text.

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 a specific action ('Get the state and output of a form detection run') and names the exact resource type. It also distinguishes this tool from the broader sibling set by noting that form detection runs have no list endpoint, so this is the run-specific retrieval path.

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 gives clear operational guidance: use it to resume a running run, inspect a failed one, and call again to keep waiting when wait budget expires. It does not explicitly name alternative tools or state when not to use it, but the context is sufficient for an agent to pick it for form detection run status/output.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct resource+action combination, and the descriptions actively disambiguate potential overlaps (e.g., extract_data vs parse_document, detect_form_fields vs edit_pdf, get_file vs get_file_upload). The consistent verb_noun prefix pattern makes the semantic boundary of every tool immediately recognizable.

Naming Consistency4/5

The dominant verb_noun pattern is highly consistent across all nine domains (list_*, get_*, create_*, update_*, delete_*, run_*, get_*_run, get_*_batch, publish_*_version). Minor deviations exist: deploy_workflow_version vs publish_*_version for the same freeze-a-draft concept, and get_form_detection_run doesn't mirror its detect_form_fields counterpart.

Tool Count2/5

86 tools is a very heavy agent-facing surface, well past the 25+ threshold. The count is inflated by the near-identical 13-tool lifecycle repeated across extract, classify, and split (each with list/get/create/update/publish/runs/batches/versions), and while each tool has a distinct purpose, the sheer volume makes selection harder.

Completeness4/5

Core lifecycles are thoroughly covered: create → update → publish → run (single and batch) → poll → cancel → delete-run → list runs/versions. Notable gaps include no delete tool for extractors, classifiers, splitters, workflows, or evaluation sets, and edit/form-detection runs have no list endpoint (documented workaround: keep run IDs). These are hygenic gaps that don't block primary workflows.

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