AIUseCaseHub

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds the 'single AI use case' scope but does not disclose output format, retention, or other behavior; this is acceptable given the read-only annotation.

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, well-structured sentence that states the action, the target, and the lookup mechanism without any wasted words. It is front-loaded and easy to parse.

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 read-by-key tool, the description plus annotations and schema provide enough context for an agent to select and invoke it. The lack of an output schema makes 'detailed information' slightly vague, but not critically 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?

The input schema covers both parameters fully (100%), including the alias relationship between id and row_key. The description adds minimal semantic value beyond restating the lookup key, so the 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 uses a specific verb ('Fetch'), a clear resource ('detailed information for a single AI use case'), and the lookup key ('by RowKey/id'). This clearly distinguishes the tool from the sibling search tools, which return lists of 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?

The phrase 'by RowKey/id returned by search tools' indicates this tool is meant to be used after finding a specific case via search. It gives clear context for when to use it, though it does not explicitly list alternatives or exclusions.

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 destructiveHint=false, so the safety profile is covered. The description adds the hybrid-ranking behavior but says nothing about result shape, pagination, access-tier limits, or other runtime behavior beyond what the schema already documents.

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 provide the ranking method and the filter surface with no filler. The core hybrid-search behavior is front-loaded before the filter enumeration.

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 schema is rich and the annotations cover safety, but the description does not explain the return value shape or how to choose among the sibling search tools. For an 18-parameter tool with no output schema, this leaves meaningful selection and expectation 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 description coverage is 100%, so all 18 parameters are already documented in the input schema. The description's filter categories add no format, constraint, or default information 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?

The description clearly states a specific verb ('Search'), resource ('AI use cases'), and distinguishing method ('hybrid full-text and vector ranking'). This separates it from the vector-only and detail-retrieval sibling tools even without naming them explicitly.

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?

No guidance is given for when to choose this tool over search_usecases, vector_search_usecases, or get_usecase_details. The filter and ranking description implies a use case, but there is no explicit selection condition or exclusion.

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 destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by noting the 'default hybrid ranking' and that the corpus is 'curated', but it does not disclose details like result format or access-tier limits, which are only present in the parameter schema.

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 two short sentences with no filler. It front-loads the core action and ranking behavior, then gives a crisp usage condition. Every 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 simple read-only search tool with a fully described schema, the description covers the essential selection criteria and behavioral expectations. It does not explain what the response contains, but no output schema exists and the main missing piece is explicit sibling differentiation, which is partially hinted at by 'default hybrid ranking'.

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 parameters are already well documented in the schema. The description adds a small amount of guidance by framing the tool around 'natural-language search term', which aligns with the search_term parameter, but it does not meaningfully extend the parameter semantics 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?

The description states a specific action ('Search curated AI use cases') and identifies the ranking method ('default hybrid ranking'), which clearly distinguishes this tool from the sibling tools hybrid_search_usecases and vector_search_usecases. The verb and resource are specific and the scope is immediately understandable.

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 to use this tool when an agent has a natural-language search term, giving a clear condition for selection. However, it does not mention when not to use it or explicitly point to alternatives such as hybrid_search_usecases or vector_search_usecases, so it stops short of full exclusion guidance.

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 destructiveHint=false, so the safety profile is covered. The description adds that the tool performs semantic vector search over AI use cases, but it does not disclose ranking behavior, similarity thresholds, or what happens with irrelevant queries. This is acceptable 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?

The description is a single concise sentence with no filler. It is slightly redundant with the title ('semantically similar' vs 'semantic vector search' and 'meaning'), but it remains efficient and front-loads the core mechanism.

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, read-only tool, the description plus schema is mostly sufficient. However, there is no output schema and no mention of return shape or ranking, and the description's 'AI use cases' vs the title's 'AI deployments' introduces a minor ambiguity that could confuse an agent.

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 parameters query and limit are fully documented in the schema. The description only reinforces that query is interpreted by meaning, which does not add meaningful new information beyond the schema. 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 states a clear mechanism ('semantic vector search') and a target resource ('AI use cases'), reinforced by the title 'Find semantically similar AI deployments'. It is specific about using query meaning, but it does not explicitly contrast itself with hybrid_search_usecases or search_usecases, relying on sibling names to imply the difference.

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 'using the meaning of the query text' implies the tool is for semantic rather than keyword matching, so an agent can infer when it might apply. However, there is no explicit guidance on when to prefer this over hybrid_search_usecases or search_usecases, nor any exclusion criteria.

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