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CKG-NVIDIA-AI

CKG-NVIDIA-AI

by WangHuikong
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Remote

get_prerequisites

Retrieve the full ordered prerequisite chain for a concept in the NVIDIA AI stack to determine what must be learned first.

Instructions

Return the full ordered prerequisite chain for a concept — everything to learn first.

Args:
    concept: Target concept — e.g. 'Speculative Decoding', 'Isaac Lab', 'NeMo Guardrails'.
    domain:  Domain name from list_domains().

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": {
      "concept": "<concept>",
      "domain": "<domain>"
    },
    "name": "get_prerequisites"
  }
}

Input Schema

NameRequiredDescriptionDefault
domainYes
conceptYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are present, so the description carries the burden. It states a read-only behavior ('Return') and mentions ordering, but doesn't disclose potential side effects, error handling, or any limitations. Since it's a simple retrieval, this is adequate but not thorough.

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, covering the purpose and parameters in two sentences. It includes helpful examples without unnecessary detail, making it easy 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 the tool's simplicity, the description is complete for basic usage. It doesn't include output format details, but no output schema exists, and the return type is implied. The mention of 'full ordered' and 'everything to learn first' sets expectations. Minor gaps include lack of edge-case guidance, but overall sufficient.

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

Parameters4/5

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

The schema has no parameter descriptions, and the description adds meaning by providing concrete examples for 'concept' (e.g., 'Speculative Decoding') and clarifying that 'domain' should come from list_domains(). This helps the agent understand valid inputs, though it doesn't explain the relationship between the two parameters in depth.

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 tool returns the full ordered prerequisite chain for a concept, which is a specific action with a clear resource. It differentiates from sibling tools like search_concepts by emphasizing the ordered chain and 'everything to learn first', though it doesn't explicitly name alternatives.

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?

It provides a clear use case (get prerequisites for a concept) but does not explicitly state when to prefer this over other tools like search_concepts or query_ckg. The 'full ordered... everything to learn first' wording implies the intended scenario, but explicit comparison is missing.

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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