CKG-NVIDIA-AI

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search_concepts

Find NVIDIA AI concepts by keyword and domain to identify dependencies and prerequisites for development tasks.

Instructions

Find concepts in a NVIDIA AI domain by keyword.

Args:
    query:  Search term — e.g. 'inference', 'sandbox', 'quantization', 'guardrails'.
    domain: Domain name from list_domains() — e.g. 'nvidia-nim', 'nvidia-openshell'.

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>",
      "domain": "<domain>"
    },
    "name": "search_concepts"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes
`domain`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.1Jul 29, 2026

TDQS

A3.5/5.0

Behavior1/5

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

No annotations are provided, and the description does not disclose any behavioral aspects such as read-only nature, side effects, or data security. Since the description is the sole source of behavioral information, its silence on these traits is a significant gap.

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 succinct, with no redundant words. It packs essential information into two short sentences and a parameter breakdown, making it easy for an agent to parse quickly.

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?

Given that an output schema exists (as indicated in the context), the description does not need to explain return formats. It provides necessary input guidance and references list_domains() for the domain parameter, making it reasonably complete for invocation.

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?

Both parameters are enriched with concrete examples and a clear source for the domain value (list_domains()). This goes far beyond the bare schema of type string and gives an agent actionable guidance on what values to provide.

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 clearly states the action 'Find concepts' and the resource 'NVIDIA AI domain' with a keyword. It is specific enough to understand the tool's primary function, though it does not explicitly differentiate it from similar sibling tools like query_ckg or ask_nvidia.

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 description provides practical usage hints, such as the domain should come from list_domains(), and gives example values for both parameters. However, it does not explicitly state when to use this tool over alternatives or when not to use it, leaving some ambiguity for an 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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