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Get the result of a user file upload

get_file_upload
Read-onlyIdempotent

Check an upload link created by request_file_upload and get the uploaded file ids (files group). Call it on the user's message after they were shown the link — never in the same turn that shared it. Default is one instant status check; wait: true blocks until the user finishes or the wait budget runs out (call again to keep waiting). Follow any llmContext guidance included in results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNotrue = block until the user completes the upload or the wait budget runs out. Default false: one instant status check.
uploadIdYesUpload session id (upl_...) from request_file_upload.
environmentYes"TEST" = the Test (development) environment, "PRODUCTION" = live. Must match a granted target from get_me (an API key pins one environment).
workspaceIdYesTarget workspace (ws_...). Must be a granted workspace — get_me lists the accepted values.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesNo
statusYes
uploadIdYes
expiresAtNo
llmContextNo
completedAtNo
completedByNo

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark it read-only/idempotent, and the description adds behavioral context beyond that: default is one instant status check, wait:true blocks until completion or budget expiry, and results may carry llmContext guidance to follow. This meaningfully informs agent expectations for polling and continuation.

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?

Three sentences with no filler: the first states what it does, the second mandates when not to call it, the third explains the wait modes and result guidance. Every sentence carries operational value and the most critical info appears first.

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?

With a full output schema present, the description correctly focuses on invocation context rather than return fields. It covers the prerequisite (request_file_upload), the correct timing relative to the user message, wait semantics, and how to handle llmContext — everything an agent needs to call it correctly.

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 description coverage is 100% and the description repeats rather than extends the wait parameter's meaning ('true = block until the user completes...'). It adds no new parameter semantics beyond what the schema already provides, so the baseline of 3 applies.

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 names a specific verb ('Check'), the exact resource ('an upload link created by request_file_upload'), and the result ('uploaded file ids (files group)'). This clearly distinguishes it from siblings like request_file_upload (which creates the link) and get_file (which fetches an existing file).

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?

It explicitly states when to call ('on the user's message after they were shown the link'), includes a hard exclusion ('never in the same turn that shared it'), and explains the wait/default behavior and retry pattern ('call again to keep waiting'). This gives the agent a precise invocation rule.

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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