AIUseCaseHub MCP

Find semantically similar AI deployments

vector_search_usecases

Read-only

Search AI use cases by semantic meaning. Enter a natural-language query to find relevant real-world AI deployments with optional result limits.

Instructions

Semantic vector search for AI use cases using the meaning of the query text.

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": {
      "query": "<query>"
    },
    "name": "vector_search_usecases"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum results requested. Public previews return at most 3; personal-key access permits up to 20.
`query`	Yes	Natural-language query.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.1Sep 7, 2026

TDQS

A3.7/5.0

Behavior4/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines3/5

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.

Deploy Server