AISOTools

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

With no annotations provided, the description must carry the burden of behavioral disclosure. It does reveal that the output is a comparison covering pricing, rating, features, pros and cons, which is useful. However, it does not state potential limitations (e.g., handling invalid tool names, missing data) or whether this is a read-only operation, but for a comparison tool, the risk is low.

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 a single, front-loaded sentence that immediately states the action ('Side-by-side comparison') and then details the specific aspects. There is no waste or unnecessary information; every word 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 low complexity (one parameter, no output schema), the description covers the purpose, the parameter meaning, and the content of the comparison. It lacks explicit error-handling details or response format, but these are not necessary for a straightforward compare operation.

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 schema already provides 100% coverage for the sole parameter: '2-5 slugs or display names.' The description repeats the '2-5' constraint but adds no additional semantics beyond the schema. Therefore, the description adds little value beyond what the structured input schema already communicates.

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 a specific verb+resource: 'side-by-side comparison of 2-5 AI tools' and lists concrete dimensions (pricing, rating, features, pros and cons). It distinguishes itself from sibling tools like search_ai_tools or get_ai_tool by focusing on comparison rather than finding or retrieving a single tool.

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 explicitly says 'Use when the user is choosing between named products,' which gives clear context and a trigger condition. It does not explicitly mention when not to use it or name alternative tools, but the sibling list and the phrase 'named products' imply that other tools are for discovering tools, not comparing known ones.

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, the description carries the full burden. It discloses the fallback behavior (curated lists first, then same-category) and gives context on how results are derived, which is more transparent than a generic 'returns alternatives' statement. It doesn't mention result ranking or any caveats, but the disclosed heuristic is valuable.

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 function, and no filler. The fallback behavior and usage guidance are packed efficiently without wasted words.

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 simple tool with 2 params and no output schema, the description is fairly complete. It covers what the tool does, when to use it, and the fallback heuristic. It doesn't explicitly describe the return format, but the name and description make it obvious that a list of alternatives is returned. A clearer mention of output (e.g., 'returns a list') would make it perfect.

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%: both 'tool' and 'limit' have clear descriptions including default and range. The tool description itself adds nothing about parameters, but the schema already handles this, so baseline 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?

The description clearly states the tool returns alternatives to a given AI tool, using curated alternative lists with fallback to same-category tools. This specific verb+resource scope distinguishes it from siblings like compare_ai_tools and search_ai_tools.

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?

Explicitly states when to use: 'Use when the user wants to replace or switch off a product.' This provides clear context and implicitly differentiates from searching, comparing, or getting details about a single tool.

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, the description carries full burden. It discloses that it returns a catalog record with listed fields, but does not mention error handling for unknown slugs, response structure, or any access restrictions. For a simple read tool, this is adequate but not rich.

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 main purpose, and the second sentence clarifies input format. No filler or redundancy.

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?

The description lists the contents of the return value (description, features, pros, cons, etc.), which compensates for the lack of an output schema. It handles the single parameter well but omits edge cases like not-found behavior or ambiguity in display name matching.

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% with the parameter description 'Slug ('claude-code') or display name ('Claude Code')'. The description repeats this acceptance rule, adding no new semantic meaning beyond the schema.

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?

Description states 'Full catalog record for one AI tool' with explicit verb and resource, and lists the exact contents. This clearly differentiates it from sibling tools like search_ai_tools which presumably return lists, and compare_ai_tools which handles multiple tools.

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 implies use when a complete record for a single tool is needed, and the sibling context reinforces that. However, it does not explicitly state when not to use it or name alternatives (e.g., 'for a list, use search_ai_tools').

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?

No annotations are provided, so the description must disclose behavior. It mentions comprehensiveness ('Every category') and output structure (slug, subcategories, count), but does not explicitly state whether it is read-only or any performance characteristics. This is adequate but not richly transparent.

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 concise sentences: the first defines the scope and return fields, the second provides usage guidance. No unnecessary words, well front-loaded, and easy to scan.

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 there is no output schema, the description sufficiently explains what the tool returns (slug, subcategories, tool count) and how to use it. For a zero-parameter list operation, this is complete and actionable.

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 has zero parameters, so the description does not need to explain parameter semantics. The baseline for 0 params is 4, and the description avoids adding noise about none-existent inputs.

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 a specific action ('Every category in the AISOTools catalog') and details what is included (slug, subcategories, tool count). It clearly distinguishes from sibling tools like search_ai_tools or get_ai_tool by focusing on category-level metadata.

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 explicitly instructs how to use the output ('Use the slugs to filter search_ai_tools'), giving a clear context for when this tool is valuable. It does not name alternatives to avoid, but the relationship to search_ai_tools provides enough guidance for a simple listing tool.

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, the description carries the full burden of behavioral disclosure. It explains the output as 'ranked summaries with the canonical aisotools.com page,' which is useful context about the return format. However, it does not explicitly state read-only behavior or any limitations, but this is largely implied for a search tool.

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 compact sentences. The first states the core purpose and output, and the second gives usage guidance. No wasted words; information is front-loaded and highly scannable.

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 a simple search tool with fully documented schema and no required parameters, the description provides sufficient context: what it searches, how results are formatted, and when to use it. The absence of an output schema is compensated by the clear return description. The description is complete for effective tool selection and invocation.

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%, so the schema fully documents each parameter. The description only paraphrases the parameters (keyword, category, pricing model) without adding new meaning, such as examples or default behavior. Thus it remains at the baseline 3.

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 searches the AISOTools catalog by keyword, category, and pricing model, and returns ranked summaries with canonical pages. This distinguishes it from sibling tools like compare_ai_tools, get_ai_tool, and find_ai_tool_alternatives, which serve different purposes.

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 provides a clear directive: 'Use this first when the user asks which AI tool does something.' This gives explicit context for when to use the tool, though it does not explicitly mention alternatives or when not to use it, which prevents a 5.

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