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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds genuinely useful behavioral context beyond those annotations: the operation is deterministic, performs privacy scrubbing, and produces field-level evidence hashes. These are non-obvious traits that help an agent understand side effects and output characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

A single sentence of about 20 words packs the core operation, target input, output entities, determinism, privacy behavior, and evidence hashes with no filler. The most important action is front-loaded, and every clause adds information.

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

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

With an output schema present, return-value details are not required. However, the description does not address base_url's role or how include_types affects output, which are meaningful for correct invocation. The tool has only three parameters, so the missing semantics create a notable gap in contextual completeness.

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

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

Schema description coverage is only 33%, with only 'html' fully described. The description partially compensates by naming the entity types that map to include_types, but it does not explain the purpose of base_url (e.g., resolving relative URLs) or the filtering semantics of include_types. Thus the description adds some meaning but leaves key parameter semantics to inference.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Normalize') and resource ('caller-supplied inline Schema.org JSON-LD') and enumerates the exact output entity types (Organization, Product, Service, Article, Event). This clearly distinguishes it from sibling tools like data.schema-validate and data.schema, which do validation and schema lookup rather than normalization.

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

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

The description implies the tool should be used when the caller supplies inline Schema.org JSON-LD and wants normalized entities, but it does not explicitly state when to prefer this over related tools such as data.schema-validate or research.feed-normalize. No exclusions or alternative routing is provided, so guidance is only implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.