RatedWithAI accessibility checker

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds the meaningful behavioral fact that the response is explanatory guidance with a code example rather than a scan result, which is what an agent needs to pick correctly. Remaining gaps (e.g. behavior for unknown rule ids) are minor given the output schema.

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, zero filler, with the positive usage case front-loaded and the exclusion immediately after. Every clause earns its place by either selecting or de-selecting the tool.

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?

Single required parameter, full schema coverage, and an output schema that carries the return shape. The description supplies the one thing structured fields can't: when this tool applies versus the scanning sibling.

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% and the schema already documents rule_id with the color-contrast example, so the description mostly echoes it. The extra sample values (image-alt, label, link-name) reinforce that the parameter is a rule id rather than a display name, but add little beyond 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?

States a specific verb (explains) and resource (an axe-core accessibility rule) plus what the answer contains ('plain-English steps and a code example'). The contrast with scan_accessibility is made explicit, so an agent can distinguish the two without opening either schema.

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?

Gives both directions: use when the user asks what a rule means or how to fix it, and do not use when the user wants a site checked. Names the sibling behavior ('it explains a rule and does not scan pages') that routes the agent to scan_accessibility instead.

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

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

Beyond the annotations (readOnly/openWorld/idempotent/non-destructive), it discloses mechanics and cost the agent cannot infer: it loads the live page in a real browser, runs axe-core, crawls up to two additional same-site pages, and takes 15-40 seconds. The extra-page crawl and the runtime are exactly the behavioral facts an agent needs for planning.

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?

Front-loaded with the usage condition, followed by behavior, runtime, and exclusions in a single tight paragraph. Every sentence carries distinct information; no filler or repetition of the title.

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?

Even though an output schema exists, the description still summarizes the return shape (0-100 score, letter grade, failing rules with example elements) and states runtime and crawl limits. For a one-parameter, open-world scanning tool, an agent has everything needed to select and call it 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 coverage is 100% for the single url parameter, so the schema already documents format and examples. The description adds the meaningful constraint that the URL must be public (no login, no localhost/private network), but otherwise restates rather than extends the schema, which is the baseline-3 case.

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 names a specific verb and resource (scan a public website/web page for accessibility) and pins the scope to concrete standards (WCAG 2.1/2.2 AA, ADA, EAA, Section 508). It is functionally unmistakable next to the only sibling, explain_accessibility_issue, since one performs a scan and the other explains a finding.

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?

It gives an explicit trigger (user wants a public site checked for accessibility) and explicit exclusions: login-gated pages, localhost/private-network addresses, and pasted HTML. The when-not conditions are the ones an agent would otherwise get wrong, so nothing is left to inference.

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