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DocImprint Document Intelligence

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Check Claims

document.check_claims
Read-onlyIdempotent

Validate factual claims against document text. Submit extracted text and a list of assertions to receive supported, contradicted, or not-found verdicts with cited evidence.

Instructions

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
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds substantial behavioral context: AI model quality, citation-level evidence, return structure with statuses and confidence, and the `truncated` flag. It also clarifies that max_tokens truncates input (in schema) but adds the return-level `truncated` indicator. No contradictions with 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?

The description is well-structured and front-loaded: a clear purpose sentence, usage alternatives, workflow, return shape, and example prompts. Every section adds value without redundancy. It is longer than a minimal description but all content is used.

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?

For a tool with 3 parameters and a structured output, the description covers all necessary context: when to use, how to use, what it returns, and examples. The return schema is shown in the description, so the agent fully understands behavior before invoking the tool. Given the existing output schema, this 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 description coverage is 100%, with each parameter (text, claims, max_tokens) already described in detail. The description does not add new parameter semantics beyond the schema, only example prompts that illustrate usage rather than parameter meaning. Baseline of 3 is appropriate because the schema carries the full load.

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 first sentence states a specific verb and resource: 'Verify a list of factual claims against document text.' It also distinguishes from siblings by mentioning 'citation-level evidence' and explicitly comparing to url.qa and collection.ask for different use cases.

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 (validate specific assertions after extraction), when not to use (open-ended questions → url.qa; multi-document → collection.ask), and gives a typical workflow from extraction to checking claims. This is clear guidance with named alternatives.

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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