Validate JSON-LD syntax

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

Annotations already carry the safety profile (readOnly, idempotent, non-destructive), so the description's added value lies in the local-execution trait and the precise failure modes it reports (empty blocks, JSON syntax errors). Those behaviors are not derivable from the schema or annotations, and the semantic-validation exclusion further reduces surprise.

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?

Two sentences and roughly two dozen words. The main action and expected results are front-loaded, with the scope exclusion in a compact second sentence. No filler or redundant restating of the tool name.

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?

For a single-parameter tool with full annotations and a provided output schema, the description covers input, scope, execution context, action, and non-goals. The only unaddrressed boundary is HTML that contains no JSON-LD blocks at all, a minor gap given the output schema likely handles it.

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%: htmlContent is already documented as 'Raw HTML containing JSON-LD script blocks.' The description's phrase 'supplied HTML' adds no parameter-level information beyond the schema, so the baseline of 3 applied.

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?

States a specific verb and resource ('Parses JSON-LD blocks in supplied HTML') and names the concrete reported outcomes ('empty blocks or JSON syntax errors'). The explicit exclusion of vocabulary semantics sets it apart from the broader validation and metadata siblings despite the generic 'schema' name.

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?

Provides clear context: it operates on locally supplied HTML and targets syntactic validation, which helps an agent decide when to reach for it. The closing limitation ('does not validate vocabulary semantics') functions as an implicit when-not-to-use, though it stops short of naming a specific sibling tool for that job.

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