DocImprint

Extract Structured Data

document.extract_structured

Read-onlyIdempotent

Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, document.parse_invoice (prebuilt schema) may be simpler. For general summarization, use document.summarize instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { : value }, data_cited: { : { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts:

"Extract the contract date, parties, and penalty amount from this agreement."
"Pull the vendor name, PO number, and total from this document."
"Get me all named fields from this form using my custom schema."

Input Schema

TableJSON Schema

Name	Required	Description
`text`	Yes	Document text to extract from. Obtain via document.extract_text or url.extract. Example: "This Service Agreement is entered into on 2025-03-15 between ACME Corp and Beta Inc..."
`schema`	Yes	Field map: describe each field you want extracted with a type hint. Example: { "total_usd": "number", "vendor": "string", "invoice_date": "ISO date YYYY-MM-DD" }
`max_tokens`	No	Input length cap (1 token ≈ 4 chars). Default ~2500 tokens. Truncates input, not output. Example: 3000

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes
`data_cited`	Yes

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. Description adds 'Uses a quality AI model with retry logic' and clarifies truncation behavior ('Truncates input, not output'), which adds value beyond 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?

Front-loaded with purpose, then usage, format, return, examples. Every sentence earns its place; no wasted words.

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 3 params, output schema inline, and clear usage guidance, the description is complete for an agent to select and invoke correctly.

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%, baseline 3. Description adds schema format examples, return structure, and example prompts, which significantly enriches understanding beyond the schema descriptions.

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?

First sentence clearly states verb (extract), resource (typed fields from document text), and mechanism (caller-defined schema). Differentiates from siblings like document.parse_invoice and document.summarize.

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 ('need specific data points'), provides simpler alternatives for invoices and summarization, and includes example prompts.

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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