ckg-nvidia-ai

route_query

Routes an NVIDIA AI query to the optimal model tier based on dependency graph depth, returning the model, reasoning approach, and context subgraph for the call.

Instructions

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

Name	Required	Description	Default
`domain`	No
`question`	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

A4.4/5.0

Behavior4/5

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

With no annotations, the description must carry full weight. It explains the deterministic graph-based routing, the absence of heuristics, and the return format. Yet it does not state whether the operation is read-only or if any side effects occur, which is a minor transparency 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 concise and well-organized, using paragraphs and a clear table to convey routing rules. Every sentence adds value with no redundant information, making it easy to parse.

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 includes output fields and parameter semantics, which is sufficient for a basic router. However, it lacks a formal output schema, examples, or error-handling details, leaving some edge cases unaddressed for an agent fully relying on this definition.

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 provides only names and types, but the description adds meaningful semantics for both parameters: 'question' is defined as a concept name or natural language query, and 'domain' is tied to list_domains(). This compensates for the lack of schema descriptions, though more detail could be given.

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 clearly states the tool's function: routing an NVIDIA question to the optimal model and reasoning approach based on graph depth. It distinguishes itself from siblings by explicitly referencing the CKG graph and introducing a unique routing mechanism, making its purpose unambiguous.

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?

The routing table provides explicit logic for how to use the output, and it references list_domains for domain values. However, it does not explicitly mention when to use this tool versus a sibling like ask_nvidia, leaving some ambiguity about selection criteria.

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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