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Queue a document for conversion with OCR and tables

submit_conversion_job

Queue a document up to 25 MB and 200 pages for conversion with OCR and tables.

Pass the file as base64 in content_base64, or as a download link in file, and its name in filename (.pdf, .docx, .pptx, .xlsx, .html, .htm or .txt). Returns job_id, status and result_url. Call get_conversion_job with the job_id until status is succeeded or failed. A conversion may take up to about 2 minutes.

Optional webhook_url: a public http or https URL that receives a POST with the job's final state. The request carries X-Appkit-Signature: sha256=<hex>, an HMAC-SHA256 of the raw body keyed with the returned webhook_secret (shown only once). 3 delivery attempts.

Requires an API key. The credits are refunded if the job fails. The uploaded file is deleted when the job finishes. Results are kept for 7 days after submission.

Credits: 10 per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNoThe file as a download link, instead of `content_base64`. In ChatGPT, pass the user's uploaded file here. Other clients can set `download_url` to a public https URL.
filenameYesThe file name with its extension (.pdf, .docx, .pptx, .xlsx, .html, .htm or .txt). The extension decides how the file is read.
webhook_urlNoOptional. A public http or https URL that receives the job's final state.
content_base64NoThe file content, base64-encoded. Up to 25 MB before encoding.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • addedInput schema / properties / content_base64 / anyOf
      Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
    • addedInput schema / properties / content_base64 / default
      Added value: +null
    • removedInput schema / properties / content_base64 / type
      Removed value: -"string"
    • addedInput schema / properties / file
      Added value: +{
+  "default": null,
+  "description": "The file as a download link, instead of `content_base64`. In ChatGPT, pass the user's uploaded file here. Other clients can set `download_url` to a public https URL.",
+  "properties": {
+    "download_url": {
+      "description": "A public https URL of the file.",
+      "type": "string"
+    },
+    "file_id": {
+      "description": "An identifier for the file. ChatGPT sets it; other clients may pass any label, such as the file name.",
+      "type": "string"
+    },
+    "file_name": {
+      "type": "string"
+    },
+    "mime_type": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "download_url",
+    "file_id"
+  ],
+  "type": "object"
+}
    • changedInput schema / required
      Previous value: -[
-  "content_base64",
-  "filename"
-]New value: +[
+  "filename"
+]
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only flag non-idempotent, open-world, non-destructive, non-read-only; the description goes far beyond that with limits, the ~2 minute duration, API-key requirement, credit cost and refund-on-failure policy, file deletion on completion, 7-day result retention, and webhook signature/HMAC/secret-shown-once/3-retry semantics. This is exactly the kind of behavior an agent must know before issuing a paid, non-idempotent job.

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?

Five short paragraphs are front-loaded with the key constraints and each covers a distinct concern (submission, polling, webhook, lifecycle, cost). Minor redundancy exists where the 25 MB limit and allowed extensions restate the schema, but no sentence is filler.

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?

Despite an output schema existing (so return fields need no explanation), the description still names the returned `job_id`/`status`/`result_url` to anchor the polling loop. Combined with limits, auth, cost, retention and webhook contract, nothing an agent needs to invoke this correctly is missing.

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, and the description adds genuine value by explaining the two mutually exclusive ingestion paths (`content_base64` vs. `file`) and the webhook's signature and retry contract. The 25 MB limit and extension list partially repeat schema text, keeping it just below a 5.

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 opening sentence states a specific verb and resource (queue a document for conversion) with concrete scope limits (25 MB, 200 pages) and the processing features (OCR, tables). It also routes the agent forward to `get_conversion_job`, which cleanly separates this async queueing tool from its polling sibling.

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 a clear operational workflow (submit, then poll `get_conversion_job` until `succeeded`/`failed`, or supply a `webhook_url`) and names the follow-up tool explicitly. It never states when to choose this over the sibling `convert_document`, so the alternative-selection guidance is incomplete, but the usage context itself is unambiguous.

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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