AIUseCaseHub

Search AI deployments with filters

hybrid_search_usecases

Read-only

Search AI use cases with hybrid full-text and vector ranking. Supports provider, industry, geography, technology, customer, partner, and boolean case-type filters.

Input Schema

TableJSON Schema

Name	Required	Description
`isRag`	No	Filter for retrieval-augmented generation deployments.
`limit`	No	Maximum results requested. Public previews return at most 3; personal-key access permits up to 20.
`query`	Yes	Natural-language query.
`country`	No	Filter by country code, for example US or GB.
`isVoice`	No	Filter for voice AI deployments.
`industry`	No	Filter by the deployment industry.
`isAvatar`	No	Filter for AI avatar deployments.
`isVision`	No	Filter for computer vision deployments.
`isCopilot`	No	Filter for copilot deployments.
`isAgentCase`	No	Filter for AI agent deployments.
`isFineTuning`	No	Filter for model fine-tuning deployments.
`partner_name`	No	Filter by implementation partner.
`customer_name`	No	Filter by the company adopting AI.
`cloud_provider`	No	Microsoft, AWS, GCP, or comma-separated values.
`isMultiAgentCase`	No	Filter for multi-agent deployments.
`isMicrosoftFabric`	No	Filter for Microsoft Fabric deployments.
`technologies_used`	No	Filter by technology name.
`isSustainabilityCase`	No	Filter for sustainability use cases.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed16 schema fields changedSep 6, 2026
- addedInput schema / properties / country / description
```
Added value: +"Filter by country code, for example US or GB."
```
- addedInput schema / properties / customer_name / description
```
Added value: +"Filter by the company adopting AI."
```
- addedInput schema / properties / industry / description
```
Added value: +"Filter by the deployment industry."
```
- addedInput schema / properties / isAgentCase / description
```
Added value: +"Filter for AI agent deployments."
```
- addedInput schema / properties / isAvatar / description
```
Added value: +"Filter for AI avatar deployments."
```
- addedInput schema / properties / isCopilot / description
```
Added value: +"Filter for copilot deployments."
```
- addedInput schema / properties / isFineTuning / description
```
Added value: +"Filter for model fine-tuning deployments."
```
- addedInput schema / properties / isMicrosoftFabric / description
```
Added value: +"Filter for Microsoft Fabric deployments."
```
- addedInput schema / properties / isMultiAgentCase / description
```
Added value: +"Filter for multi-agent deployments."
```
- addedInput schema / properties / isRag / description
```
Added value: +"Filter for retrieval-augmented generation deployments."
```
- addedInput schema / properties / isSustainabilityCase / description
```
Added value: +"Filter for sustainability use cases."
```
- addedInput schema / properties / isVision / description
```
Added value: +"Filter for computer vision deployments."
```
- addedInput schema / properties / isVoice / description
```
Added value: +"Filter for voice AI deployments."
```
- addedInput schema / properties / limit / description
```
Added value: +"Maximum results requested. Public previews return at most 3; personal-key access permits up to 20."
```
- addedInput schema / properties / partner_name / description
```
Added value: +"Filter by implementation partner."
```
- addedInput schema / properties / technologies_used / description
```
Added value: +"Filter by technology name."
```
First observedSep 6, 2026

TDQS

A3.5/5.0

Behavior3/5

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.

Conciseness5/5

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.

Completeness3/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines2/5

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.

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TDQS

A3.6/5.0

Disambiguation2/5

Three of the four tools are search variants with overlapping behavior: search_usecases and hybrid_search_usecases both use hybrid ranking, and vector_search_usecases partially overlaps with the default semantic behavior. The descriptions provide some clues (filters vs. natural-language), but the boundary between the search tools remains unclear.

Naming Consistency4/5

The search tools follow a clean pattern of [modifier_]search_usecases, and get_usecase_details uses a standard verb_noun form. The only minor deviation is singular 'usecase' in get_usecase_details versus plural 'usecases' in the search tools.

Tool Count4/5

Four tools is a reasonable size for a focused use-case hub. Having three search variants is slightly redundant, but the count is still well within an appropriate scope.

Completeness4/5

The set covers the core retrieval workflow: search across multiple modes and fetch full details by ID. Minor gaps include the absence of a browse/list-all tool or explicit pagination, but agents can likely accomplish the intended tasks.

Resources

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