audit_ai_search_readiness

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

With no annotations, the description carries the full burden: it does disclose meaningful behavior — what artifacts it fetches, a 0-100 scoring model, and that it auto-generates a /llms.txt and JSON-LD snippet. However, it never says whether the tool only reads or also writes/deploys the generated file, whether it makes live network requests, how long it takes, or what happens on an unreachable domain. That ambiguity around side effects is a real gap for a tool that 'auto-generates a ready-to-deploy' file.

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, front-loaded with the core capability and followed by the concrete inspection targets and deliverables. Some mildly promotional padding ('Complete 2026', 'ready-to-deploy') could be trimmed, but the density of useful information is high and nothing is buried.

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 tool with no output schema, the description covers the essential ground: what gets audited, what the score means, and what deliverables come back. The main missing piece is the response shape (score breakdown vs. generated files) and any failure behavior, but the agent has enough to invoke 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?

There is a single required parameter, and schema coverage is 100% — the schema already documents `domain` with an example. The description adds nothing about accepted formats (bare domain vs full URL with path, scheme handling) beyond what the schema states, so the baseline of 3 is appropriate.

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 and resource — scores a domain 0-100 on readiness for named AI answer engines — and enumerates exactly what it inspects (/llms.txt, /llms-full.txt, robots.txt permissions for named bots, JSON-LD entities). This is clearly distinct from sibling tools like enrich_company_domain or inspect_domain_infra, which do domain enrichment or infrastructure checks rather than AEO auditing.

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 framing ('Complete 2026 AEO/GEO auditor', 'Scores any domain') strongly implies when it applies, but there is no explicit when-to-use statement, no prerequisites (does the domain need to be live?), and no routing to or away from the sibling tools. An agent can infer the use case but must guess at boundaries.

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