AISOTools

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

Annotations already declare readOnlyHint, openWorldHint, non-idempotent and non-destructive, so the safety profile is covered. The description adds real behavioral context beyond that — 10-30 second latency, 24h cache reuse, run-to-run variance, and that results come from one assistant. It stops short of explaining how the 0-100 score is computed or what the response shape contains, leaving those gaps against an otherwise rich disclosure.

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 trigger, then method, then caveats, then exclusions — a good structure. It is on the longer side, and the latency/caching detail could be tighter, but almost every sentence carries decision-relevant information.

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 an output schema present and 100% param coverage, the description need only frame usage and behavior, which it does. Latency, caching, variability, exclusions, and what is reported are all covered — nothing an agent needs to call it correctly is missing.

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%, so the baseline is 3. The description adds meaning the schema alone doesn't: it explains that the url is read as a homepage and that category is inferred from it, reinforcing the role of the optional param and the fallback behavior.

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?

Specific verb and resource: reads a product's homepage, asks ChatGPT five buying questions, and reports which ones named the product plus a 0-100 score. It also embeds user-voice examples ('does ChatGPT mention my product?') that map intent to tool, which distinguishes it clearly from the sibling directory tools (search_ai_tools, get_ai_tool, etc.).

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 explicit when-to-use triggers via example utterances and explicit when-not cases: general AI tool questions, private/login-only sites, and search-engine ranking. It also notes the 24-hour cache so the agent understands a repeat call may return stored results rather than re-running.

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 readOnlyHint=true, idempotentHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is fully covered structurally. The description adds no behavioral context beyond restating the 2-5 item bound already in the schema — nothing about what happens with unknown or mixed slug/display-name input. Adequate but not additive.

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 short sentences, zero filler, with the capability front-loaded and the usage cue trailing. Every clause 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?

For a one-parameter read-only tool with no output schema, enumerating the returned dimensions effectively substitutes for a return spec. The only residual gap is error behavior when a name is not recognized, which is minor here.

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% and the single `tools` parameter is documented as "2-5 slugs or display names." The description repeats the 2-5 count and adds the useful constraint that products must be named, but supplies no extra format or normalization detail. Baseline 3 applies when the schema does the work.

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?

Names a specific verb and resource (side-by-side comparison of AI tools) and enumerates the comparison dimensions (pricing, rating, features, pros/cons), so the agent knows exactly what comes back. It stops short of naming a sibling like search_ai_tools or get_ai_tool, so the boundary with those is inferable rather than explicit.

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?

"Use it when the user is choosing between named products" gives a clear selection condition that implicitly separates it from discovery tools like search_ai_tools. There is no explicit when-not clause or named alternative for the ambiguous case (one product, or products not yet identified).

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 read-only, idempotent, closed-world behavior, so safety needs no explanation. The description contributes real behavioral context beyond that: results come from curated lists first and degrade to same-category tools, which tells the agent what quality of answer to expect. It does not say what happens when no alternatives exist.

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 with no filler, and the sourcing behavior is stated before the usage trigger. The opening is a noun fragment rather than a sentence, a minor structural rough edge but not wasteful.

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 two-parameter read-only lookup with no output schema, the description covers what it returns, where results come from, fallback behavior, and the user intent that triggers it. Nothing essential to calling it correctly is missing.

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 both parameters (tool, limit) are documented in the schema, including the 1-20 range and default of 8. The description adds nothing about slug vs display-name handling or how limit interacts with the fallback, 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 the resource (alternatives to a given AI tool) and the two sourcing strategies (curated alternative lists, same-category fallback), which separates it from get_ai_tool and search_ai_tools. The verb is implied by the noun phrase rather than stated, but the intent is unmistakable.

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?

"Use it when the user wants to replace or switch off a product" gives a clear trigger condition. It stops short of naming sibling alternatives such as compare_ai_tools or search_ai_tools and when those would be preferred, so a little inference remains.

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 readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description contributes the returned field set and the slug-or-display-name input flexibility, but says nothing about failure behavior for unknown tools or whether the record is ever truncated.

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 payload contents and ending with the input format. Nothing is redundant or padding, and the most decision-relevant information leads.

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 correctly compensates by enumerating the returned fields (description, features, pros/cons, pricing tiers, alternatives). The remaining gap is edge-case behavior for misses or ambiguous names in a read-only, single-parameter tool, which is minor.

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% and the single parameter already documents the slug/display-name convention, so the description's restatement of that same fact adds no new meaning. Baseline 3 applies; no format examples or normalization rules beyond the schema are given.

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 pairing ('Full catalog record for one AI tool') and enumerates the record's contents, which separates it from the plural-listing siblings. However, it never names search_ai_tools or find_ai_tool_alternatives as the alternative for lookup-by-criteria, so the differentiation is only implicit in the word 'one'.

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 is only implied: 'one AI tool' suggests a lookup when the tool is already known, versus search_ai_tools for discovery. There is no explicit when-to-use, when-not-to-use, or named alternative, and no guidance on what happens if the identifier is ambiguous or unknown.

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 readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds value by disclosing the response contents (slug, subcategories, per-category tool counts), though it omits ordering, pagination, or whether subcategories are nested.

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 front-loaded sentence naming the scope and payload with no filler. Every clause 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?

With no output schema, the description rightly explains what the call returns, which is the main completeness risk for a zero-arg list tool. Minor gaps remain around ordering and whether the list is exhaustive or paginated.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. No parameter-level confusion is possible.

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 the resource (every category in the AISOTools catalog) and even the fields returned (slug, subcategories, tool count), so an agent knows exactly what it gets. It does not differentiate from siblings like search_ai_tools, which would require an explicit 'use this to discover category slugs for filtering' statement.

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 is only implied: as a catalog-listing tool it is naturally the discovery entry point before search_ai_tools or get_ai_tool, but the description never says when to call it or when another sibling is preferable. No exclusions or prerequisites are 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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description goes well beyond them: it discloses the return shape (ranked summaries with canonical URLs) and the pagination contract with matched vs returned vs truncated, including a direct warning not to overstate catalog coverage. This is exactly the extra context annotations cannot express.

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 sentences, front-loaded with the search scope, then the return value, then the truncation caveat. No filler; every clause carries operational meaning.

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?

There is no output schema, so the description must describe return values — and it does, including the ranked-summaries structure and pagination semantics. Combined with full schema coverage, an agent has everything needed to call it and to interpret the result 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% and each parameter is already documented with examples, so the baseline is 3. The description restates the limit ceiling (max 50) but adds no syntax, format, or interaction detail beyond what the schema supplies.

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 (search) plus the resource (AISOTools catalog of AI tools) and the three filter dimensions, which cleanly separates it from get_ai_tool, compare_ai_tools, and find_ai_tool_alternatives. It stops short of naming any sibling explicitly, so the distinction relies on the agent inferring it.

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?

"Use it when the user asks which AI tool does something" gives a concrete triggering condition, and the truncation guidance tells the agent how to follow up. No when-not conditions or named alternatives (e.g. 'for details on a single tool, use get_ai_tool') are provided.

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