AIUseCaseHub

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

No annotations are provided, so the description carries the transparency burden. 'Fetch' clearly signals a read-only retrieval operation with no side effects, but the description does not disclose not-found behavior, permissions, or the shape of the returned details. For a simple get-by-key tool, this is an acceptable but not thorough 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?

The description is a single, front-loaded sentence with no filler. Every word contributes to identifying the action, the resource, and the lookup key, which is exactly what an agent needs for this simple tool.

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 low-complexity tool with two well-documented parameters and a clear single-record retrieval purpose, the description is nearly complete. There is no output schema, but 'detailed information' sufficiently signals the expected return payload, and the key provenance is covered in the schema.

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 already explains both parameters. The description only repeats that the tool keys on RowKey/id and does not add meaningful semantics beyond the structured parameter definitions, so the baseline score of 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 states a specific verb ('Fetch'), a clear resource ('detailed information for a single AI use case'), and the required key ('by RowKey/id'). This clearly distinguishes it from the sibling search tools, which all operate over use cases in a search capacity rather than retrieving one record by key.

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 schema explicitly says row_key is 'returned by search tools', which clearly implies this tool is the follow-up retrieval step after a search. The description does not explicitly name alternatives or state when-not-to-use, but the workflow context is clear enough from the wording and sibling 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 transparency burden. It does disclose ranking behavior (hybrid full-text + vector) and filter categories, which is useful, but it remains silent on result-set behavior (ranking score, limit/pagination, return shape) and whether any side effects exist (implied read-only by 'Search' but not stated).

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, no filler, with the core operation front-loaded in the first sentence and the filter support in the second. Every word adds value.

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 an 18-parameter tool with no annotations and no output schema, the description gives a usable overview but omits important operational details: how filters combine, whether string filters are exact/substring, the default/limit behavior, and what a result looks like. It's enough to start but not fully 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 only 11%, so the description must compensate, and it does by grouping the sparse parameters into meaningful categories: provider, industry, geography, technology, customer, partner, and 'boolean case-type filters' (covering the many is* booleans). This adds semantic structure the bare schema lacks, though it doesn't enumerate every parameter or explain combination semantics.

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 first sentence names the verb ('Search'), the resource ('AI use cases'), and the distinguishing method ('hybrid full-text and vector ranking'), which separates it from sibling search tools like vector_search_usecases and search_usecases. This is specific enough for an agent to know what the tool does.

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 hybrid search over AI use cases is needed, but it does not explicitly say when to choose this over search_usecases or vector_search_usecases, nor does it list any exclusions or alternatives. 'Supports ... filters' describes capability, not selection criteria.

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 behavioral disclosure burden. It does reveal the ranking behavior ('default hybrid ranking') and the natural-language nature of queries, but it omits any details about result format, pagination, or read-only status. This is more than a tautology but still a thin behavioral picture.

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 first sentence front-loads the core action, and the second adds an actionable usage condition. 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?

The description covers purpose and a usage trigger, and the schema handles parameter constraints. However, with siblings hybrid_search_usecases and vector_search_usecases present, the description does not explicitly clarify when to choose search_usecases over them; 'default hybrid ranking' is suggestive but not sufficient for complete routing confidence. Gaps in alternative routing make this adequate but incomplete.

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 already documents search_term and query as natural-language/alias; 67% coverage is high. The description only echoes 'natural-language search term' and adds nothing about limit or query defaults, so it stays 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?

States a specific verb and resource: 'Search curated AI use cases.' It also names the ranking mode ('default hybrid ranking'), which hints at differentiation from sibling vector_search_usecases. However, it does not explicitly distinguish from hybrid_search_usecases, so it stops short of full sibling differentiation.

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?

Provides an explicit trigger condition: 'Use this when an agent has a natural-language search term.' This is clear when-to-use context, but there are no when-not-to-use conditions or named alternatives, so it earns a 4 rather than 5.

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 must carry the behavioral disclosure burden. It does convey that search is semantic and vector-based rather than lexical, but it does not mention output format, ranking behavior, thresholds, or potential limitations. This is adequate for a simple search tool but not rich in behavioral context.

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, focused sentence with no filler. It front-loads the core action ('semantic vector search') and immediately identifies the resource ('AI use cases') and the key input behavior ('meaning of the query text').

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, with only two parameters and no output schema, so the description is mostly sufficient for basic invocation. However, it lacks explicit guidance on when to choose this tool over the sibling hybrid or regular search tools, and it does not describe what kind of results the agent should expect.

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 description reinforces the query parameter by explaining it is natural-language text and that meaning matters, which adds context beyond the schema's 'Natural-language query.' However, it does not mention the limit parameter or its constraints, leaving half of the schema semantics undocumented.

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 it performs 'semantic vector search' for 'AI use cases', identifying both the resource and the search method. It distinguishes itself from siblings like search_usecases and hybrid_search_usecases by emphasizing meaning-based vector retrieval rather than keyword or hybrid matching.

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 that this tool is appropriate when searching by meaning rather than exact terms, but it does not explicitly state when to prefer it over hybrid_search_usecases or search_usecases. No when-not-to-use guidance or alternative routing is provided.

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