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query_ckg

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
NameRequiredDescriptionDefault
depthNo
domainYes
conceptYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
      -  "properties": {
      -    "result": {
      -      "title": "Result",
      -      "type": "string"
      -    }
      -  },
      -  "required": [
      -    "result"
      -  ],
      -  "title": "query_ckgOutput",
      -  "type": "object"
      -}New value: +null
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are present, so the description carries the behavioral burden. It discloses useful non-schema behaviors: partial match support for concept, depth range 1–5, default depth 3, and the requirement that domain comes from list_domains(). It does not mention result format or traversal edge semantics, but for a read-style graph traversal tool the described behaviors are meaningful and not misleading.

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 and well-organized: a one-sentence purpose statement followed by three focused parameter annotations. There is no filler, and the most important behavioral detail (traversal from a concept) is front-loaded.

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 input side is complete: all parameters, ranges, defaults, and domain sourcing are documented. However, because there is no output schema and no description of the return shape, an agent still has some uncertainty about what a traversal result looks like. This is a minor gap given the otherwise strong parameter coverage.

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?

Schema description coverage is 0%, so the description must fully compensate. It does: each parameter is explained with concrete examples, a valid range for depth, a default value, and a source command for domain. This is exactly the kind of parameter-level detail an agent needs beyond the raw JSON 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?

The description uses a specific verb ('Traverse') and resource ('NVIDIA knowledge graph') and identifies the exact relationship type being explored ('prerequisites and dependents'). This clearly distinguishes what the tool does from generic alternatives like search_concepts or ask_nvidia, and the coverage is specific enough to separate it from get_prerequisites.

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?

Usage is implied through the traversal semantics and the instruction that domain should come from list_domains(). However, the description does not explicitly say when to choose this tool over siblings like get_prerequisites or query_intersect, nor does it mention when not to use it. The context is helpful but not fully decision-ready.

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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