check_crawler_accessFetch robots.txt and a bounded public page through validated redirects, then inspect named-token path rules, final delivery, X-Robots-Tag, and HTML meta robots. Named-bot edge/WAF treatment remains unobservable.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public site URL and path to evaluate (https://example.com/docs) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavior. It does: it mentions validated redirects, a bounded public page, and the specific signals inspected. It also explicitly states the limitation that 'Named-bot edge/WAF treatment remains unobservable,' which is honest and useful for setting expectations.
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 dense sentences, with the action front-loaded and the scope limitation placed at the end. Every clause adds value; there is no repetition or filler.
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 this is reasonably complete: it states what is fetched, how redirects are handled, what is inspected, and what remains unobservable. It does not describe the response shape or error/edge cases, but with no output schema this is a minor gap given the clarity of the operation.
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% for the single url parameter, so the schema already carries the meaning. The description adds context by indicating the URL is used to fetch robots.txt and a page, but it does not provide meaningful extra semantics beyond the schema 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 uses a specific verb and resource: 'Fetch robots.txt and a bounded public page' and lists the inspected elements. However, it does not explicitly distinguish itself from siblings like scan_url, so an agent must infer the difference from context.
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 explains the tool's internal procedure but gives no guidance on when to choose it over alternatives. It also lacks exclusions or conditions such as 'use this when scanning site-level AI access rules.' The WAF limitation is behavioral, not usage-selection guidance.
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.
Most tools target clearly distinct artifacts: OpenAPI, A2A cards, crawler access, saved reports, and fix verification. The main ambiguity is between scan_url and get_fix_pack, since both scan a public URL and return fix-pack-related output, though scan_url emphasizes the full score/findings while get_fix_pack focuses on prioritized fixes.
All tool names follow a consistent verb_noun snake_case pattern: audit_, check_, get_, scan_, validate_, verify_. The verbs are appropriate and the naming makes the intended action readable at a glance.
Seven tools is well-scoped for a URL and API readiness scanning server. Each tool addresses a distinct phase or artifact in the workflow: scanning, fix generation, fix verification, report retrieval, and specialized static audits.
The core lifecycle is covered: scan a URL, obtain fixes, verify fixes, and retrieve reports, plus specialized audits for OpenAPI, A2A, and crawler access. Minor gaps exist, such as no way to list or manage saved public reports and no explicit repository-connection tool, but these are workable.