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

Name	Required	Description
`text`	Yes	Document 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."
`claims`	Yes	Factual statements to verify. Each claim is checked independently against the text. Example: ["Founded in 2010", "Revenue exceeded $1M"]
`max_tokens`	No	Input length cap (1 token ≈ 4 chars). Default ~3000 tokens. Truncates input text, not the output. Example: 4000

Output Schema

TableJSON Schema

Name	Required	Description	Default
`claims`	Yes
`truncated`	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. Description adds that it uses a 'quality AI model with citation-level evidence' and mentions truncated flag in output. 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?

Concise and well-structured: purpose, when to use, alternatives, workflow, return format. No unnecessary 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?

Covers all aspects: purpose, usage, workflow, return schema, examples. With only 3 params and output schema provided, description is complete.

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 clear descriptions. Description adds example prompts and typical usage but does not deepen parameter meaning beyond schema. Adequate.

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?

Clearly states 'verify a list of factual claims against document text' with specific verb and resource. Distinguishes from sibling tools url.qa (open-ended questions) and collection.ask (multi-document investigation).

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 recommends use after document.extract_text or url.extract, and gives alternatives: url.qa for open-ended questions, collection.ask for multi-document. Typical workflow provided.

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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