aicatalog

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

With no annotations, the description must fully convey behavioral traits. It states the tool retrieves full details but does not mention any side effects, authentication needs, or limitations. The description is straightforward but lacks depth on expected behavior or return characteristics.

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 sentence of 13 words, immediately stating the purpose and key detail (slug origin). No unnecessary words; every phrase 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 simple get-by-identifier tool with one parameter and no output schema, the description is complete. It specifies the input (slug), how to obtain it (from search_ai_tools), and the output (full details). The sibling tools are listed, providing additional context.

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 covers 100% of the parameter with description 'Tool slug, e.g. 'notion-ai'.' The description adds value by connecting the slug to 'search_ai_tools', providing operational context. This extra information helps the agent understand the parameter's origin, making semantics clearer than schema alone.

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 verb 'Get' and the resource 'full details for one AI tool', specifying identification by slug. It references the sibling tool 'search_ai_tools' to clarify how the slug is obtained, effectively distinguishing itself from the siblings.

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 when to use this tool: after obtaining a slug from 'search_ai_tools'. It provides context on the slug's origin but does not explicitly state when not to use it or give alternative tools for different use cases.

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 behavioral traits. It states that the tool lists all categories, but does not mention any side effects, authentication requirements, or performance considerations. For a simple read-only list with no parameters, this is adequate but lacks depth.

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, clear sentence that immediately states the tool's purpose. It is front-loaded with no unnecessary words, earning 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 simple listing tool with no output schema, the description adequately explains what it does. It might benefit from mentioning the format or structure of the returned categories, but given the simplicity, the description is sufficiently complete.

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 no parameters, and the schema coverage is 100% (empty). The description adds meaning by specifying that it lists categories, which is appropriate for a parameterless tool. The baseline for zero parameters is 4, and the description meets that.

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 that the tool lists all AI-tool categories, which is a specific verb-resource combination. It distinguishes from siblings: 'get_tool' likely retrieves a single tool, and 'search_ai_tools' allows filtering, while this tool provides a comprehensive list of categories.

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 implicitly suggests using this tool when you need a complete list of categories. It does not explicitly exclude alternatives or mention when not to use it, but the context is clear given the sibling tool names.

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 does disclose useful behavior: the catalog size (25,000+), keyword-based matching, and the exact return fields (name, tagline, catalog URL, pricing). However it says nothing about result ordering/relevance, pagination behavior beyond the limit param, or rate limits.

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 tight sentences with zero filler: what it searches, how large the corpus is, and what comes back. The purpose is front-loaded in the first clause.

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 two-parameter search tool with no output schema, the description covers purpose, corpus scope, and return fields, which is close to sufficient. The remaining gap is ranking/ordering behavior and how to escalate from search results to a full tool record via get_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% and both parameters are documented in the schema, including the limit range (1-25) and an example query string. The description's 'by keyword' adds no syntax or format detail beyond what the schema already provides, so 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 (Search) and resource (the AICatalog directory of 25,000+ AI tools) and scopes it to keyword lookup, which implicitly separates it from get_tool (single lookup) and list_categories (browsing). It never names those siblings, so the differentiation is left to inference rather than stated.

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?

There is no explicit when-to-use guidance, no statement of prerequisites, and no mention of the sibling tools it competes with (get_tool, list_categories). An agent must infer from the name alone that this is the discovery path and get_tool the retrieval path.

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