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ai_evidence_verify_hash

Verify that a given text matches the expected hash of an AI evidence record by computing its SHA-256 and comparing to the stored content_hash.

Instructions

Compute SHA-256 over the canonical UTF-8 form of candidate_text (LF endings, no trailing newline) and compare against the evidence's verification.content_hash. Returns ok=true on match, or { error: hash_mismatch, expected, recomputed }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
document_jsonYes
candidate_textYesThe text whose hash should match the evidence's content_hash.
Behavior4/5

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

The description explains the hashing algorithm (SHA-256, canonical UTF-8 form) and the comparison logic. It does not contradict annotations (none provided). It lacks explicit statements about side effects or permissions, but for a read-only verification tool, the transparency is good.

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 two sentences with no fluff. It directly states the operation, canonical form requirements, and return values. Highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple hash verification tool with two parameters and no output schema, the description covers the main logic and return format. It does not fully document the document_json parameter or error cases beyond hash mismatch. Minor gaps, but generally 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 50% (only candidate_text has a description). The tool description adds value by specifying how candidate_text is used (hash computation) and implying that document_json contains evidence, but it does not explain document_json's role or structure. Thus, description partially compensates but leaves a gap.

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 action: compute SHA-256 over candidate_text and compare to evidence's content_hash. It returns ok=true or an error object with details. This is specific and distinct from sibling tools like disclosure_verify_artifact_hash.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for verifying hash integrity of evidence text, but it does not explicitly state when to use this tool versus alternatives like disclosure_verify_artifact_hash or attestation_verify. No guidance on prerequisites or conditions is 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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