api

Parse Invoice

document.parse_invoice

Read-onlyIdempotent

Parse a receipt or invoice document into structured fields. Uses a quality AI model for accuracy. Use when you need to extract line items, totals, and merchant info from financial documents. For general document text, use document.extract_text instead. Returns: { invoice: { merchant, date (YYYY-MM-DD), line_items[], subtotal, tax, total }, cited: { : { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts:

"Parse this invoice and give me the line items and total."
"Extract the merchant, date, and amounts from this receipt."
"Read this scanned invoice and return structured data."

Input Schema

TableJSON Schema

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

Output Schema

TableJSON Schema

Name	Required	Description	Default
`cited`	Yes
`invoice`	Yes

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, indicating safe read-only behavior. The description adds context about model quality and the return structure, which is useful but not required. No contradictions.

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?

The description is well-structured with a clear first sentence, usage guidance, return format, and examples. It is moderately sized; every sentence adds value, though the return schema could be slightly shortened.

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 annotations and output schema documentation (return block), the description is fully complete: it covers purpose, usage, return structure, and examples. No gaps remain for an AI agent to select and use this tool 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?

The input schema has 100% description coverage for both parameters (mime_type and document_base64). The description does not add new parameter-level details beyond what the schema provides, 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 parses invoices/receipts into structured fields, using a quality AI model. It distinguishes itself from the sibling tool document.extract_text by specifying use for financial documents versus general text.

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?

The description explicitly states when to use (extract line items, totals, merchant info) and when not to (use document.extract_text for general text). It provides example prompts that illustrate typical use cases.

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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