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 covered. The description adds that it returns 'detailed information' for a single record but does not describe the response structure, fields, or any constraints beyond identification.

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, focused sentence that states the purpose in a front-loaded manner. There is no filler, irrelevant repetition, or unnecessary detail.

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 fetch with one required parameter and clear annotations, the description is complete enough for an agent to select and invoke it correctly. The absence of an output schema is not a gap here, since the tool's purpose is self-explanatory.

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 provides 100% coverage of both parameters, including an alias relationship between id and row_key. The description's mention of 'by RowKey/id' simply mirrors the schema and adds no deeper semantic or formatting guidance.

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 action ('Fetch detailed information'), the resource ('a single AI use case'), and the identifier needed ('by RowKey/id'). This distinguishes it from the search-related sibling tools, which are for discovering rather than reading a specific 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?

The description makes it clear this tool is for retrieving a specific use case when a RowKey/id is already known, implicitly distinguishing it from the search siblings. However, it does not explicitly state when not to use it or name those alternatives.

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 the tool read-only and non-destructive. The description adds meaningful behavioral context by disclosing the hybrid ranking mechanism, which is not visible in the annotations or schema. It does not cover rate limits or result shape, but the read-only annotation lowers the burden for safety-related details.

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 no fluff: the first fronts the core action and ranking behavior, the second summarizes the available filter dimensions. 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?

The rich schema fully documents all 18 filter parameters, including the limit nuance about public previews. The absence of an output schema is acceptable because 'Search AI use cases' clearly implies a list of matching use cases. The main missing piece — explicit routing against sibling tools — is already accounted for in the usage-guidelines dimension.

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 documents every parameter. The description only summarizes filter categories at a high level and adds no syntax, format, or default information beyond the schema, keeping it at the baseline of 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?

The description states a specific action ('Search AI use cases') with a clear resource, and the phrase 'hybrid full-text and vector ranking' distinguishes it from the sibling tools. The title reinforces scope with 'with filters'.

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 guidance is implied by the phrase 'hybrid full-text and vector ranking' — this suggests it is the choice when both retrieval modes are wanted. However, the description never explicitly tells an agent when to choose this tool over search_usecases or vector_search_usecases, nor does it name alternatives.

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, and the description adds that it uses default hybrid ranking and searches curated content. It does not disclose limits, rate constraints, or other behavioral details, but it does not contradict the annotations.

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 sentences with no filler: the first states the action and method, the second gives the usage trigger. Every word contributes to agent decision-making.

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 read-only search tool with fully documented parameters, this description covers core usage and ranking behavior adequately. It lacks explicit guidance for choosing this over the sibling hybrid_search_usecases, but that is not critical for correct invocation.

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 100% of parameters with descriptions, so the description does not need to explain parameters in depth. The phrase 'natural-language search term' aligns with search_term but adds no new semantic 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 the action ('Search'), the resource ('curated AI use cases'), and the specific method ('default hybrid ranking'), which distinguishes it from the vector and hybrid sibling tools. It is specific and actionable.

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 'Use this when an agent has a natural-language search term,' providing a clear trigger for when to select this tool. It does not explicitly mention alternatives or exclusions, so it stops short of a full 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?

Annotations already declare the tool read-only and non-destructive. The description adds useful behavioral context by clarifying that it performs vector-based semantic matching rather than keyword or hybrid search. No contradictions or hidden side effects are present.

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. The only minor inefficiency is that 'semantic' and 'using the meaning' overlap slightly, but the statement remains clear and compact.

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, read-only search tool, the description plus schema provides enough information to invoke it correctly: a natural-language query and an optional limit. The title and description clarify that the result is a set of similar AI deployments, so no critical invocation detail 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?

The input schema fully documents both parameters—query and limit—so the description does not need to repeat them. It adds no additional parameter-level meaning, but with 100% schema coverage, 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 identifies a specific operation—semantic vector search—and a clear resource (AI use cases). It also indicates the matching is based on meaning, which helps distinguish it from keyword search, though it does not explicitly name the sibling tools.

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 semantic-similarity use, but the description does not explicitly state when to prefer this tool over search_usecases or hybrid_search_usecases. Usage guidance is thus more implied than explicit.

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