DocImprint

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

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds the return format (Markdown) and structure ({ pages: number, text: string }). It does not cover edge cases like missing tables, but given annotation coverage, this is acceptable.

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 (3 sentences plus return type and example prompts), front-loaded with purpose, and every sentence adds value. No extraneous information.

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 simplicity (2 params, 100% schema coverage, output schema present, annotations provided), the description covers all necessary aspects: purpose, usage guidelines, return format, and examples. It is complete for effective selection and invocation.

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 has 100% coverage with clear descriptions and examples for both parameters (mime_type, document_base64). The description does not add further detail beyond the schema, so baseline score of 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 the tool's purpose: 'Extract tables and forms as Markdown from a PDF or image (base64-encoded).' It further distinguishes itself from the sibling tool document.extract_text by specifying the appropriate use case for structured tabular data.

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.' Also provides an 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

A4.5/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, grouped by domain (account, bundle, collection, document, job, receipt, url). Descriptions and naming make it easy to differentiate between similar tools like url.extract vs document.extract_text or collection.search vs collection.ask.

Naming Consistency5/5

All tools follow the same prefix.group_action pattern in snake_case (e.g., account.quota, bundle.get, collection.create). No mixing of conventions, making the API predictable and easy to navigate.

Tool Count5/5

With 22 tools, the server covers a comprehensive set of operations for document and evidence management. Each tool serves a specific purpose, and the count feels well-scoped without being bloated or sparse.

Completeness3/5

The tool surface lacks explicit create and delete operations for bundles and collections. Bundles appear to be created externally, and there is no tool to remove a bundle or collection. This is a notable gap given the server's stated purpose.

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