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AIUseCaseHub MCP

AIUseCaseHub MCP

by flo7up
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Search enterprise AI deployments

search_usecases
Read-only

Find relevant AI use cases by searching with natural language queries. Returns ranked results from curated sources.

Instructions

Search curated AI use cases using the default hybrid ranking. Use this when an agent has a natural-language search term.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "search_term": "<search_term>"
    },
    "name": "search_usecases"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum results requested. Public previews return at most 3; personal-key access permits up to 20.
queryNoAlias for search_term.
search_termYesNatural-language search term.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.1

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, 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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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