KitRank use cases

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered and the bar is lower. The description earns credit by disclosing the exact return payload (setup time, difficulty, step-by-step instructions, copyable prompt), which is genuinely useful since there is no output schema. It does not mention behavior on an unknown/stale id, which keeps it from a 5.

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 dense sentence that front-loads the core fact ('full recipe for one use case id') and then lists the payload. Every item listed is informative, though the trailing enumeration is a long run-on that could be trimmed slightly.

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 and only one required parameter, the description does the necessary work of describing what comes back, which is the main completeness risk for a detail-lookup tool. What remains missing is minor: failure mode for an invalid id and any sense of response size.

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% for the single id parameter, so the schema already documents both the type and where the id comes from. The description only restates that the input is one use case id, adding no format details (e.g. id shape) beyond the schema. 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 (retrieve the full recipe) and resource (one use case id) and enumerates the exact contents returned: source credit, claimed result, named tools, cost note, difficulty, step-by-step setup. This clearly separates it from the sibling list/search tools, which return collections rather than a single full detail record.

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: the 'one use case id' phrasing and the id parameter's provenance note signal that this is the detail-follow-up to a list/search call, but that provenance lives in the schema rather than the description. There is no explicit when-to-use/ when-not-to-use statement or named alternative inside the description text.

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 and openWorldHint=false, so safety is covered. The description usefully adds the sort key (source publish date) and direction (newest first), but says nothing about pagination, result caps, or whether the window is a hard cutoff — gaps that matter for a listing endpoint.

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 with no filler; the ordering rule and the intended use are front-loaded and each sentence 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 two-optional-parameter read tool with no output schema, the description conveys the shape of the result (a recency-ordered list) but omits how many items come back and how the days/limit knobs behave. Adequate but with clear gaps.

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 0%: both 'days' and 'limit' have no descriptions, only types/defaults/bounds. The phrase 'this week' loosely maps to the days=7 default, but the description never explains the day-window parameter or what limit does. With low coverage, the description should compensate and largely does not.

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 ('browse') plus the resource (use cases) and adds the ordering semantics ('by source publish date, newest first'), so the agent knows this is a recency-sorted listing. It does not explicitly contrast itself with search_use_cases or use_cases_by_tool, but the browsing framing implicitly separates it from a search 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?

'Use to browse what builders shipped this week' gives a concrete usage context, but there is no statement of when-not-to-use it or which sibling to pick for filtered/lookup queries (get_use_case, search_use_cases, use_cases_by_tool). Usage is implied rather than routed.

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 and openWorldHint=false, so safety is covered. The description adds return-content context (8 items with ids, blurbs and counts), which is valuable since there is no output schema, though it omits any ordering or stability notes.

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 payload description front-loaded and the routing instruction second. 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 input parameters and no output schema, the description carries the return-value burden and does so by listing the fields returned. Combined with the sibling routing cue, an agent has everything needed to call it 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?

The tool takes no parameters, so per the baseline this scores 4. There is nothing for the description to disambiguate, and it correctly conveys that the call needs no arguments.

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 (List) and resource (KitRank categories), quantifies the scope (the 8 categories), and names the returned fields (ids, blurbs, use-case counts). This clearly distinguishes it from data-fetching siblings like get_use_case and search_use_cases.

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 directs the agent to call this before filtering search_use_cases by category, naming a concrete sibling and the condition that selects it. No inference required.

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 (readOnlyHint=true, openWorldHint=false), so the description's job is to add context. It does so by enumerating return fields and emphasizing results are 'real, sourced' with verification and source URL, which tells the agent about data provenance. It omits pagination/limit behavior, which is a minor gap.

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 purpose, then query guidance, then return shape. The list of five example queries is slightly long but serves a real disambiguating function for query style; no filler sentences.

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 appropriately enumerates the return fields, and it addresses the required query parameter with examples. It does not explain the limit/category parameters or sibling selection, but for a three-parameter search tool the coverage is nearly 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?

Schema coverage is 67%, so the schema documents the query and category params, and the description adds concrete query examples ('faceless youtube shorts', 'cancel subscriptions with AI') that clarify expected input granularity and style. The limit parameter and category enum values are not elaborated on, so it stops short of full coverage.

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 (real, sourced AI agent use cases) with a clear scope ('people actually shipped'). It does not explicitly name or contrast with siblings like latest_use_cases or use_cases_by_tool, so it is clear in isolation but not differentiated from alternatives.

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 gives query-formulation guidance ('Prefer concrete goals people type into Google') with five examples, which implies how to use it. However, it never states when to choose this over siblings such as use_cases_by_tool or latest_use_cases, leaving the routing decision 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds the matching semantics (tool names and aliases), but says nothing about return shape, ordering, or the 25-item cap behavior beyond the schema. 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 tight sentences with the scoping definition front-loaded and the usage cue second; every clause earns its place and no filler is present.

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 filtered list with no output schema, the description covers what is returned (use cases) and when to reach for it. It could note result ordering or whether multiple tools can be combined, but nothing critical 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 only 50% — the 'tool' param is documented but 'limit' has no description. The description compensates partially by clarifying that the tool param accepts ids, common names, and aliases (claude-code, n8n, Cursor, etc.), but it adds nothing about limit semantics or matching behavior (exact vs. fuzzy).

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 (List) and resource (use cases) with a precise scoping condition (those naming a consumer tool), which naturally distinguishes it from latest_use_cases and search_use_cases. It does not explicitly name a sibling or contrast with one, so it falls just short of the top score.

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?

"Useful when the user already picked a stack" gives a clear triggering condition for choosing this tool over the broader search/list siblings. There is no explicit when-not guidance or named alternative, but the context is unambiguous.

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