Fetch a URL, extract every JSON-LD () block, and validate basic structure — @context/@type presence plus required fields for common schema.org types (Article, Product, Organization, WebSite, LocalBusiness, BreadcrumbList, FAQPage). Reports per-block errors rather than failing the whole call on one bad block.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to check |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses key behaviors: fetching every JSON-LD block, validating specific structural requirements, and reporting per-block errors instead of failing the whole call. It does not mention edge cases like no JSON-LD found or network failures, but covers the core behavior well.
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 information-dense sentence with no filler. It front-loads the primary action, then adds validation specifics and error-behavior nuance without redundancy.
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 one-parameter validator with no output schema, the description adequately covers input, extraction, validation targets, and error reporting. The exact return structure isn't spelled out, but 'Reports per-block errors' conveys the output shape sufficiently.
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?
The only parameter, 'url', has full schema description coverage ('The URL to check'). The tool description adds little beyond 'Fetch a URL', so it doesn't meaningfully augment the schema. Baseline 3 applies.
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 resource (URL), the action (fetch, extract, validate), and the validation scope (@context/@type plus required fields for specific schema.org types). It distinguishes itself from the sibling 'structured_data_extract' by its validate focus, though it doesn't name that sibling explicitly.
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 this tool is for validating JSON-LD structure on a URL, but it gives no explicit when-to-use guidance or comparison with alternatives. The per-block error handling is a behavioral cue rather than a usage guideline.
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.
Many tools audit overlapping site signals (seo_audit vs structured_data_extract vs tech_stack_fingerprint; page_performance_check vs pagespeed_insights; ssl_cert_check vs ssl_labs_grade; broken_link_check vs sitemap_url_validator), so an agent could initially pick the wrong one. Descriptions usually clarify the distinction, but the boundaries are not always obvious.
Most names follow a snake_case target+operation pattern (ssl_cert_check, email_deliverability_check), but check_open_ports and check_robots_sitemap reverse the order, and the action suffixes vary widely (check, audit, validate, lookup, extract, grade, report, insights). Still readable, but not a single predictable convention.
At 29 tools, the server is above the 25-tool threshold and feels like an undifferentiated grab bag of single-purpose audits rather than a tightly scoped toolkit. Many checks could be consolidated (e.g. the separate SSL and performance tools, or domain_report versus its component checks).
For a web/domain/email/security diagnostics toolbelt, the coverage is unusually broad: DNS, TLS, email, SEO, structured data, vulnerabilities, ports, redirects, and more are all represented. Minor gaps exist (no generic HTTP request/debug tool, no zone-transfer or full WHOIS history), but agents can accomplish most diagnostic workflows without hitting dead ends.