Audit JSON-LD schema

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context: read-only when given url (one HTTP GET), zero network when given schema_json, deterministic and rule-based (no LLM), and conflict resolution (schema_json wins). This adds value beyond annotations without contradiction.

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?

The description is well-structured with clear sections and front-loads the main purpose. It uses bullet-like sentences for behavioral points. It is not overly long, but could be slightly more concise (e.g., 'Deterministic, rule-based; no LLM.' is efficient). Overall earns its sentences.

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?

Given the input schema (3 params, 100% coverage), annotations (readOnly, idempotent, destructive), and existence of an output schema, the description covers all essential aspects: purpose, usage context, behavioral traits, parameter constraints. It provides sufficient context for an AI agent to select and invoke the tool correctly.

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 100%, so baseline is 3. The description does not add much beyond schema parameter descriptions, but it clarifies the either-or constraint and the fact that schema_json wins over url. These additions are minor, so score remains at baseline.

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 clearly states the tool validates JSON-LD structured data against Schema.org rules and AI-citation best practices. It distinguishes from sibling 'audit_page' by noting that this tool is for focused JSON-LD audits versus full page audits. The verb 'validate' and resource 'JSON-LD schema' are specific.

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?

Explicitly states when to use: 'focused JSON-LD audits, or to validate a schema block you're about to ship.' Also specifies when not to use: 'For a full page audit that includes schema + everything else, use audit_page instead.' Additionally, clarifies that either url or schema_json must be provided, not both, with a win condition.

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