ckg-nvidia-ai

query_ckg

Traverse the NVIDIA knowledge graph from a specific concept, listing its prerequisites and dependents up to a chosen depth within a selected domain.

Instructions

Traverse the NVIDIA knowledge graph from a concept — prerequisites and dependents.

Args:
    concept: Concept name (partial match supported) — e.g. 'TensorRT', 'NIM', 'Isaac Lab'.
    domain:  Domain name from list_domains() — e.g. 'nvidia-tensorrt-triton', 'nvidia-isaac'.
    depth:   Traversal depth 1–5 (default 3).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`depth`	No
`domain`	Yes
`concept`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.1Sep 7, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It explains the main behavior (traversing the graph), but does not disclose side effects, output format, limits, or error behavior.

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 concise and well-structured, with a one-sentence purpose followed by clear per-parameter examples and constraints. No filler or redundancy.

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?

The description covers the necessary invocation parameters and behavior. Since an output schema exists, the absence of return-value details is acceptable. It could mention traversal direction or result interpretation, but not critically.

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?

Schema description coverage is 0%, but the description adds meaningful guidance for each parameter: partial match for concept, reference to list_domains() for domain, and valid range/default for depth. This compensates well for the bare schema.

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 ('Traverse') and resource ('NVIDIA knowledge graph'), along with the core purpose ('prerequisites and dependents'). Clearly distinguishes from siblings like list_domains and get_prerequisites by focusing on graph traversal.

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

Usage Guidelines2/5

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

Gives no explicit when-to-use guidance. With sibling tools like get_prerequisites and search_concepts, the description does not explain when query_ckg should be preferred or how it differs from those alternatives.

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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