AEO Inspector

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

Adds substantial behavior beyond the annotations: expected latency (2-4 minutes), a billing guarantee (not charged unless at least 7 of 10 engines answer), the payment mechanism ($9 USDC via x402 in _meta["x402/payment"]), and error semantics (invalid input or failed run returns an error and is not charged). This is exactly the operational context annotations cannot carry.

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-loads what the report contains and keeps every sentence load-bearing (engines, output fields, timing, billing, payment). It is a dense single block rather than cleanly separated structure, which costs it a point, but there is no 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?

With no output schema, the description must enumerate return values and it does: per-engine answers, mention rate, site citations, visibility score, competitor share (overall and per engine), cited sources, and report_url. Combined with the payment, latency, and error disclosures, an agent has everything needed to call and interpret this tool.

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% — all three parameters, including the brand and topic defaults, are already documented in the schema. The description adds no format or syntax detail beyond that, so the 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?

States a specific verb+resource (generate a 10-engine AI visibility report) and quantifies scope precisely: five category questions asked 3 times across 10 named engines, yielding 150 answers. That level of specificity makes it unambiguous what this tool produces versus a lighter sibling like scan_site.

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 decision-relevant context — 2-4 minute runtime and a $9 USDC charge — which lets an agent judge when the call is worth making, and states the non-charge condition. It does not explicitly name scan_site or pricing as the cheaper alternative to use instead, so the when-not guidance is implied rather than stated.

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?

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: guaranteed no input, no network request, and no payment/charge, which is exactly the cost-risk information an agent needs before calling.

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 tight sentence with the key qualifier ('Free') front-loaded. There is mild redundancy between 'Free' and 'no payment', but no filler otherwise.

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?

With no output schema, the description must convey what comes back, and it does: prices of paid tools and endpoints plus the payment network and address. It is complete for a zero-arg introspection tool, with only minor ambiguity about the exact return shape.

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 are 0 parameters, so the baseline is 4. The description confirms 'No input', reinforcing that this call is argument-free, but there is nothing further to document.

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 states the resource precisely: prices of the paid tools and HTTP endpoints, plus the payment network (Base) and receiving address. An agent can distinguish this metadata tool from siblings scan_site and full_report, though the verb is only implied rather than stated (it reads as a noun phrase with no 'returns'/'gets').

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?

'Free' and 'No input, no network request, no payment' imply this is a zero-cost discovery call, which suggests calling it before invoking paid tools. However, it never explicitly says when to use this versus the siblings or that it should be used to check costs before a paid call.

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?

Annotations cover the safety profile (readOnly, openWorld, non-idempotent, non-destructive), and the description adds substantial context they cannot: 10-25s latency, $0.05 USDC cost via x402 with the payment location, and the no-charge policy on invalid input or failed runs.

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?

Purpose is front-loaded, but the body is one enormous semicolon-chained sentence packing dozens of checks into a single breath, which hurts scannability. Length is somewhat justified by genuine complexity, but the structure would benefit from grouping.

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?

With no output schema, the description fills the gap by enumerating the return payload (100-point score, prioritized fixes, result_url, badge_url, paste-ready kit) plus cost, timing, and payment channel. Complete enough for an agent to invoke and interpret results.

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 single url parameter is already 100% documented in the schema with format and example, so the schema does the heavy lifting. The description adds no syntax or constraint detail beyond the schema, and mentions non-parameter behavior (crawling up to 9 pages) instead.

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+resource ("Deep AEO scan of a website") and enumerates exactly what gets fetched, diffed, and diagnosed. An agent can distinguish this from a generic scan without opening the 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?

Usage context is implied by the scope (a deep AEO scan), but the description never says when to prefer this over the sibling full_report or when the lighter/cheaper path applies despite naming alternatives in the toolset. No explicit when-not guidance, so usage is only implied.

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