NVIDIA NemoClaw CKG

route_query

Route a NemoClaw question to the optimal model and reasoning approach via graph depth.

The CKG graph IS the router. NemoClaw's dependency chains (e.g. OpenShell → L7Proxy → CorporateCA → mTLS) are deep and typed — hop depth is a deterministic complexity signal. No heuristic: the graph decides which model and reasoning approach to use.

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

Args: question: Concept name or natural language question about NemoClaw / OpenShell.

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

Input Schema

TableJSON Schema

Name	Required	Description	Default
`question`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

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

First observedJul 30, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description carries full burden. It explains the routing logic and returns, but does not mention side effects, authorization needs, or whether the tool is read-only. The description is somewhat transparent but not fully.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a summary, explanatory paragraph, routing table, and Args/Returns. It is generally concise but includes some technical jargon (e.g., 'OpenShell → L7Proxy → CorporateCA → mTLS') that could be minimized.

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 complexity (routing based on graph depth), the description provides sufficient context: the routing logic, the table, and the return fields. The output schema exists, so the description's return info is a helpful supplement.

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 sole parameter 'question' lacks schema descriptions (0% coverage). The description adds meaningful context: it specifies that the question can be a concept name or natural language query about NemoClaw/OpenShell, which guides the agent.

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 verb 'Route' and the resource 'NemoClaw question', and distinguishes from siblings by explaining the unique graph-depth-based routing approach. The routing table further concretizes the purpose.

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 description explains the deterministic complexity signal via hop depth, giving context for when to use this tool. However, it does not explicitly state when not to use it or name alternatives, leaving some room for ambiguity.

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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