validate_schema_markupUse this for a focused JSON-LD syntax check of supplied HTML. It parses JSON only and does not validate Schema.org vocabulary semantics.
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | Raw HTML markup supplied by the user; this is not a webpage URL. |
| Name | Required | Description | Default |
|---|---|---|---|
| error | No | ||
| issues | Yes | ||
| overview | Yes | ||
| truncated | Yes | ||
| total_issues | Yes | ||
| blocks_checked | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey read-only, idempotent, non-destructive behavior. The description adds useful behavioral context beyond annotations by clarifying that only JSON is parsed and Schema.org vocabulary semantics are out of scope.
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 two sentences with no filler. It front-loads the use case and immediately states a key limitation, making it efficient and easy to parse.
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 read-only tool with a rich output schema and strong annotations, the description covers the core scope and limitations. Nothing critical needed to decide whether to invoke the tool is missing.
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 coverage is 100%, and the parameter description already explains that html is raw markup, not a webpage URL. The tool description reinforces the input type but adds little new semantic detail beyond the schema.
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 identifies the specific action: a focused JSON-LD syntax check of supplied HTML. It also distinguishes itself from semantic Schema.org validation, which helps an agent differentiate it from siblings like validate_html and validate_css.
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 states when to use the tool: for a focused JSON-LD syntax check. It also provides an explicit exclusion by noting it does not validate Schema.org vocabulary semantics, though it does not directly name alternative tools or conditions for them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools are mostly distinct by input mode (live URL vs supplied HTML/CSS) and check scope (HTML, CSS, JSON-LD, SEO signals, links). The main ambiguity is between audit_public_webpage and generate_validation_report, which run nearly the same checks but differ only in fetching vs supplied HTML; their descriptions call this out clearly.
All names follow a lowercase snake_case verb_noun pattern, but the set mixes four verbs (audit, check, generate, validate) for closely related validation operations. This is readable and predictable, with only minor semantic inconsistency around validate_schema_markup and generate_validation_report.
Eight tools is well within the ideal range and each covers a distinct input type or validation focus. The count feels appropriate for a web-validation server without redundancy bloating the surface.
The domain is well covered: live-page and sitemap audits, raw HTML/CSS/JSON-LD validation, SEO/accessibility signals, and broken links are all represented. Minor gaps exist, such as no dedicated accessibility validator and limited link-checking behavior, but agents can accomplish the core workflows.