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Glama

NVIDIA AI CKG

by io.github.Yarmoluk

route_query

Route an NVIDIA AI question to the optimal model and reasoning approach via graph depth.

The CKG graph IS the router — hop depth is a deterministic complexity metric.
Deeper NVIDIA prerequisite chains (CUDA → TensorRT → TensorRT-LLM → NIM) require
more capable models. No heuristic: the graph decides.

Routing table:
  hop_depth 1  → haiku  · direct          (simple lookup)
  hop_depth 2  → sonnet · generic_cot     (moderate chain)
  hop_depth 3+ → opus   · sparql_cot      (deep dependency, structured reasoning)

Args:
    question: Concept name or natural language question about NVIDIA AI.
    domain:   Domain from list_domains() — e.g. "nvidia-tensorrt-triton", "nvidia-nim".

Returns:
    model_tier + reasoning_approach + why + context subgraph to inject before LLM call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNo
questionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

……

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

……

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?

……

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?

……

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?

……

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

All NVIDIA AI CKG toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes: listing, searching, traversing, intersecting, routing, and verifying are all separated. The main potential confusion is between get_prerequisites and query_ckg, since both traverse prerequisite relationships, though the former is explicitly ordered and full-chain while the latter is general graph traversal.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: ask, get, list, query, route, search, verify. Even the query_* pair is consistent, and the one brand-tinged name (ask_nvidia) still follows the same verb_noun structure.

Tool Count5/5

Nine tools is a well-scoped set for an NVIDIA knowledge-graph server. There are enough tools to cover discovery, querying, specialized traversal, routing, and verification without feeling bloated or thin.

Completeness4/5

The surface covers domain listing, concept search, graph traversal, prerequisite chains, conjunctive queries, routing, NL question answering, and source verification—strong coverage for a knowledge-graph query server. A minor gap is the lack of a direct get-concept-details tool, though query_ckg and ask_nvidia partially compensate.

Resources