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by com.docimprint

Check Claims

document.check_claims
Read-onlyIdempotent

Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after document.extract_text or url.extract when you need to validate specific factual assertions. For open-ended questions about a document, use url.qa instead. For multi-document investigation, use collection.ask. Typical workflow: document.extract_text/url.extract → document.check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts:

  • "Check whether this contract mentions a liability cap of $1M."

  • "Verify these claims against the document: [claims list]."

  • "Does the report actually say revenue grew 23%?"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesDocument text to check claims against. Obtain via document.extract_text or url.extract. Example: "ACME Corp was founded in 2010. Revenue exceeded $1M in 2024."
claimsYesFactual statements to verify. Each claim is checked independently against the text. Example: ["Founded in 2010", "Revenue exceeded $1M"]
max_tokensNoInput length cap (1 token ≈ 4 chars). Default ~3000 tokens. Truncates input text, not the output. Example: 4000

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimsYes
truncatedYes

TDQS

A5/5.0
Behavior5/5

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

Annotations already show readOnlyHint and idempotentHint true, but the description adds that it uses a quality AI model with citation-level evidence, explains max_tokens truncates input not output, and details the return format. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and usage, then return format, then examples. Every sentence adds value, no filler. It is appropriately sized for the tool's complexity.

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 parameters and a detailed output schema included in the description, the context is complete. It explains workflow, alternatives, return format with example prompts. No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds significant value: it explains where to get text (extract_text/url.extract), gives examples for claims and max_tokens, and clarifies max_tokens truncates input. This exceeds minimal compensation.

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 it verifies factual claims against document text, using a quality AI model with citation-level evidence. This specific verb-resource combination distinguishes it from siblings like url.qa (open-ended questions) and collection.ask (multi-document).

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?

It explicitly says to use after document.extract_text or url.extract, provides a typical workflow, and names alternatives (url.qa for open-ended, collection.ask for multi-document). This gives clear when-to-use and when-not-to-use guidance.

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.

Resources