The AI Integration Hub

Search AI Use Cases

search_use_cases

Read-onlyIdempotent

Search 1,000+ vetted AI use cases by keyword, industry, or business workflow (e.g. 'invoice', 'dental', 'customer support'). Use when someone asks how AI can help their business, wants AI ideas for an industry, or looks for automation opportunities. Also use it to find the slug before get_agent_playbook or run_agent. Returns titles, summaries, and links. Public: no sign-in.

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": "search_use_cases"
  }
}

Input Schema

Name	Required	Description
`limit`	No	How many matches to return. Defaults to 10. Maximum 20.
`query`	Yes	Words to match. Example: invoice, patient intake, HVAC dispatch.
`industry`	No	Optional area or sub-industry slug that limits the search, such as accounting, hvac, or home-trade/hvac.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 22, 2026

TDQS

A5/5.0

Behavior5/5

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

While annotations cover the safety profile (readOnlyHint, idempotent), the description adds vital contextual behaviors such as the scope ('1,000+ vetted AI use cases'), the return data ('titles, summaries, and links'), and accessibility constraints ('Public: no sign-in').

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 information is delivered in a highly efficient manner. The first sentence is a comprehensive overview, followed by clear segmentation of use cases and workflow instructions, making it highly front-loaded and impactful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of the search parameters and the absence of an output schema, the description is maximally complete. It clarifies the return content and defines the tool's role in the overall agent pipeline, leaving no ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema coverage is 100%, the description provides high-quality semantic value by giving concrete examples ('invoice', 'dental', 'customer support') and clarifying the nuances of the 'industry' parameter, helping the agent interpret vague user requests.

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 defines the tool's purpose: searching 1,000+ vetted AI use cases. It specifies the resources (use cases) and the action (search), and it effectively distinguishes itself from siblings like run_agent or get_agent_playbook by stating its role in the discovery process.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The guidance is extremely robust. It provides multiple use case scenarios ('how AI can help', 'industry ideas', 'automation opportunities') and gives critical workflow advice, instructing the agent to use the tool to find a slug *before* invoking get_agent_playbook or run_agent.

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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