Extend MCP

Get (or wait on) an extract run

get_extract_run

Read-onlyIdempotent

Get the state and output of an extract run (extract 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). Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema

Name	Required	Description
`wait`	No	Block until terminal or budget expiry. Default false.
`runId`	Yes	The run ID returned by extract_data or list_extract_runs.
`detail`	No	"concise" (default): status, output, failure fields, dashboardUrl. "full": adds config, confidence/citations, usage, timestamps.
`environment`	Yes	"TEST" = the Test (development) environment, "PRODUCTION" = live. Must match a granted target from get_me (an API key pins one environment).
`waitSeconds`	No	Max seconds to block waiting on the run (clamped to the server wait budget).
`workspaceId`	Yes	Target workspace (ws_...). Must be a granted workspace — get_me lists the accepted values.

Output Schema

TableJSON Schema

Name	Required	Description
`file`	No
`files`	No
`runId`	Yes
`output`	No	Extracted values (PROCESSED only).
`status`	Yes	Terminal status, or "running" (resume via the get tool).
`runType`	No
`llmContext`	No
`dashboardUrl`	No
`failureReason`	No
`failureMessage`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds meaningful behavioral detail beyond annotations: wait blocks until terminal or budget expiry, repeated calls are required, and llmContext guidance in results should be followed.

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 with the core purpose, followed by the most important behavioral nuance (waiting). Every sentence contributes value, and there is no redundant restatement of the title or schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the rich input schema, annotations, and output schema, the description covers the essential operational details: what the tool does, how waiting works, and how to handle results. It could be slightly more explicit about when to choose this over related sibling tools, but it is otherwise complete for a read-only polling operation.

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?

Schema coverage is 100%, so all parameters are already documented in the input schema. The description adds useful context for the wait parameter and the overall polling pattern, but does not materially expand on runId, detail, environment, or workspaceId beyond what the schema provides.

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 retrieves the state and output of a specific extract run, with the added nuance of waiting on running runs or inspecting failed ones. This distinguishes it from list_extract_runs (listing), cancel_extract_run (canceling), and delete_extract_run (deleting).

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 provides clear context on when to use this tool: to resume waiting on a running run or to inspect a failed one. It also explains the wait behavior and that repeated calls are needed to continue waiting, though it does not explicitly name alternative tools for list/cancel/delete scenarios.

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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