HList AI Tools

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

No annotations are provided, so the description carries the full behavioral burden. 'Get full details' reasonably signals a read-only, non-destructive lookup, but there is no disclosure of what happens with an unknown slug (error vs. null), whether results are cached, or any rate limits — meaningful gaps for a zero-annotation 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?

One sentence, front-loaded with the verb and scope, with the examples placed where they help most. There is no filler and nothing an agent must scan past to reach the operative 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?

For a two-parameter lookup with no output schema and no nested objects, the description covers what the tool does and what identifies the record. It lacks error/empty-result behavior and any hint at return shape, but the tool's simplicity keeps those omissions 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 description coverage is 100%, so both the slug and the optional lang enum are already documented in the schema, and the baseline is 3. The description's inline slug examples add light confirmation of slug format but nothing about the lang parameter or fallback behavior when a translation is missing.

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?

"Get full details for a single AI tool by slug" names a specific verb (get), resource (AI tool), and scope (single, by slug), which cleanly separates it from the sibling list_categories and search_ai_tools. Concrete examples ("cursor", "stable-diffusion") reinforce what a slug looks like. It stops just short of naming the siblings directly.

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 'single ... by slug' phrasing implies this is the detail-lookup tool to use once you already have a slug, which is useful implied routing. However, it never states when to prefer it over search_ai_tools or what to do if you only have a name rather than a slug, so the when/when-not guidance is left to inference.

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 carries the burden, but for a zero-parameter listing tool there is little behavior to disclose. It does signal an unfiltered full listing and that counts are included, yet it says nothing about ordering, whether results are cached/static, or the shape of the returned categories.

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 sentence with the resource and scope front-loaded and zero filler. Every word contributes 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?

The tool is simple (no params, no output schema), and the description covers what is returned at a high level. However, with no output schema, more detail on the shape of each category entry (name, id, count field) would let an agent use the result without guessing.

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 has zero parameters, so the baseline of 4 applies; there is no parameter documentation for the description to compensate for.

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 uses a specific verb and resource ("List all AI tool categories") and adds the return content ("with tool counts"), which distinguishes it clearly from siblings get_tool and search_ai_tools that operate on tools rather than categories. It stops short of naming those siblings explicitly, which keeps it from a 5.

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 when-to-use guidance or mention of alternatives. An agent must infer that this is a discovery/browse step, perhaps before search_ai_tools, entirely on its own.

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 at all, the description still discloses genuinely behavioral facts: the fixed result cap ('top 10 matches'), the freshness of the corpus ('updated daily'), and the exact fields returned (name, description, pricing, China accessibility, GitHub stats). It omits auth requirements, behavior on zero matches, and any rate/truncation handling beyond the 10-result limit.

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, both front-loaded: the first establishes what is searched and over what corpus, the second establishes what comes back. No filler, no restatement of the tool name, and the result-shape information is placed where it is immediately useful.

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?

Because there is no output schema, the description's field list does real work, and the corpus-size/freshness note is helpful. However, 'keyword and/or category' implies a category-only call is valid while the schema marks query as required, leaving a real ambiguity an agent could trip over, and nothing is said about empty-result behavior.

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 all three parameters are already documented in the schema, and the description adds little beyond mapping 'keyword' to query and 'category' to the category filter. It says nothing about the lang parameter or about how keyword and category combine when both are supplied, 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 and resource ('Search the hlist.cc AI tools directory') with concrete scope (412 tools, updated daily) and names the facets searched (keyword and/or category). It never mentions the sibling tools get_tool or list_categories, so an agent must infer that this is the discovery entry point rather than the detail lookup.

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 phrase 'by keyword and/or category' implies the search use case, but there is no explicit when-to-use guidance, no statement of what this tool should not be used for, and no pointer to get_tool for full details or list_categories for the taxonomy. Usage is left to inference from the purpose sentence.

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