Deduped AI use cases

search_use_cases

Read-only

Search Deduped's catalog of practical AI use cases generated from hot GitHub AI repos. Returns ids, titles, summaries and page urls. Then call get_use_case for steps and an agent prompt.

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
`query`	Yes	The task, e.g. 'turn PDFs into a searchable knowledge base'
`category`	No	Optional category filter, e.g. Research, Content, Software and DevOps

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 22, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds real value by naming the return fields (ids, titles, summaries, page urls) and the chained follow-up call, though it is silent on ranking, pagination beyond the limit param, or result quality.

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?

Three tight sentences: what it searches, what it returns, and what to do next. The workflow hint is front-loaded right after the return shape, with no filler.

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?

With no output schema, naming the returned fields is important and present, as is the follow-up routing to get_use_case. It falls short only on result-ordering/relevance behavior and how category values are discovered, which an agent might still wonder about.

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 coverage is 67%: query and category carry inline descriptions, while limit is documented only via min/max/default. The description frames the domain (practical AI use cases) but adds no syntax or filtering nuance beyond the schema, so the baseline 3 applies.

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?

States a specific verb (search) and resource (Deduped's catalog of practical AI use cases from hot GitHub AI repos), plus the exact fields returned. It also distinguishes itself from the sibling get_use_case, which handles steps and prompts.

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?

Explicitly says to call get_use_case afterward for steps and an agent prompt, giving a clear two-step workflow. It does not clarify when to prefer this over the other siblings (hot_repos, today_news), so the routing guidance is partial rather than complete.

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

All Deduped AI use cases tools Inspect

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