api

Extract Tables

document.extract_tables

Read-onlyIdempotent

Extract tables and forms as Markdown from a PDF or image (base64-encoded). Use when the document contains structured tabular data such as financial statements, data sheets, or forms. For plain prose documents, use document.extract_text instead. Returns: { pages: number, text: string } — text contains Markdown-formatted tables. Example prompts:

"Extract the tables from this financial statement."
"Pull the data table from this PDF into Markdown format."
"Get the tabular data from this form document."

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mime_type`	Yes	MIME type of the document. Example: "application/pdf" for PDF bank statements, "image/jpeg" for photo of a form.
`document_base64`	Yes	Base64-encoded PDF or image bytes (max ~15 MB). Example: "JVBERi0xLjcNJeLjz9MNCj..." (truncated PDF base64)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes
`pages`	Yes

TDQS

A4.7/5.0

Behavior5/5

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

The annotations (readOnlyHint=true, idempotentHint=true) declare the tool as safe and idempotent. The description adds value by disclosing the return format '{ pages: number, text: string }' with Markdown-formatted text, the base64 encoding requirement, and a maximum document size of ~15 MB. No contradictions with annotations.

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 concise, with clear front-loading of purpose, followed by usage guidelines, return format, and example prompts. Every sentence serves a purpose with no 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 tool's low complexity (2 parameters, simple input/output), the description fully covers functionality, input constraints (base64, size, MIME types), output format, and appropriate use cases. It even includes example prompts for common user intents.

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% with detailed descriptions for both parameters (mime_type enum with examples, document_base64 with example and size limit). The description does not add significant new meaning beyond the schema; it mentions base64 encoding generally but doesn't elaborate further. Baseline 3 is appropriate.

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 'Extract tables and forms as Markdown from a PDF or image (base64-encoded).' It specifies the verb (Extract), the resource (tables and forms from PDF/image), and the output format (Markdown). It also distinguishes itself from the sibling tool document.extract_text, which handles plain prose.

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?

Explicitly states when to use: 'Use when the document contains structured tabular data such as financial statements, data sheets, or forms.' It also provides a clear alternative: 'For plain prose documents, use document.extract_text instead.'

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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