dropcoin-a11y

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

No annotations are provided, so the description carries full responsibility. It discloses that this is pure text generation (no network access), that missing facts become placeholders, that legal review is required, and that it costs credits and needs an API key. This is exceptionally transparent and goes beyond basic purpose.

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 long, front-loaded with purpose, and covers usage, behavior, and constraints without fluff. Every sentence earns its place.

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?

Given the tool's complexity (4 params, nested object, no output schema, no annotations), the description covers the key aspects: purpose, optional input, placeholders, legal caveat, and access requirements. It does not explicitly state the output structure or error handling, but for a text-generation tool this is acceptable.

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 0%, so the description must compensate. It mentions the optional scan_result but does not explain the purpose of url, contactEmail, or organisation. While parameter names are self-explanatory, the description does not provide meaningful additional semantics for each parameter.

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 states the tool's function: drafting a German accessibility statement for a website. It uses a specific verb ('Draft') and resource, and distinguishes itself from the sibling tool 'scan_accessibility' by mentioning optional enrichment with a previous scan result.

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 context is clear: this is for drafting after scanning, optionally using scan_accessibility results. It implies the tool should be used in conjunction with scanning, but does not explicitly state when not to use it or name alternatives. Still, the guidance is sufficient for an agent.

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?

With no annotations provided, the description carries the full transparency burden and exceeds expectations. It discloses concrete behavioral traits: catches roughly 57% of violations, uses a headless browser, results are cached 6h per (url, standard), a cache hit still costs the full price, requires 500 credits and an API key. These caveats give the agent realistic 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?

Three dense sentences, each earning its place. The first sentence front-loads the core function, the second and third add critical operational caveats. No fluff or repetition of schema fields.

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?

Given the tool's complexity (4 params, no annotations, no output schema), the description covers pricing, auth, caching, detection limits, and a key exclusion. It falls short only on explaining the return format and the two less-obvious parameters, which prevents a perfect score.

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 0% for 4 parameters, so the description must compensate. It does clarify 'url' by requiring a public https:// URL and indirectly hints at 'standard' through WCAG 2.1/2.2 AA, but it never explains 'waitMs' or 'includePasses'. The agent would have to guess from parameter names alone, which is insufficient for low coverage.

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 action ('Automated technical accessibility pre-check'), a concrete resource (one public https:// URL), and attaches meaningful scope (WCAG 2.1/2.2 AA via axe-core, mapped to German BFSG requirements). It also distinguishes itself from the sibling tool 'draft_statement' by framing itself as a pre-check rather than the final statement product.

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 clearly positions the tool as a preliminary step and explicitly says it is 'NOT a substitute for a manual audit and never a conformity proof.' This is a strong when-not guideline. However, it does not explicitly mention using the sibling tool 'draft_statement' as the alternative next step, stopping short of a full 5.

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