ckg-nvidia-ai

ask_nvidia

Answers natural-language questions about the NVIDIA AI stack with responses grounded in a knowledge graph.

Instructions

Ask a natural-language question answered by Qwen grounded on the NVIDIA CKG.

Requires Ollama running locally with a Qwen model pulled:
    ollama pull qwen2.5:14b

Override model:  NVIDIA_CKG_MODEL env var  (default: qwen2.5:14b)
Override host:   NVIDIA_CKG_OLLAMA env var (default: http://localhost:11434)

Args:
    question: Natural-language question about the NVIDIA AI stack.
    domain:   Domain from list_domains() — auto-detected from question if omitted.

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

B3.4/5.0

Behavior3/5

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

The description discloses important behavioral prerequisites, such as requiring Ollama running locally with a specific Qwen model, and it explains the env var overrides. However, it does not mention failure modes, latency, or whether the operation is read-only, and no annotations are provided to fill these gaps.

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 mostly concise and front-loads the core purpose. The setup instructions and env var overrides are relevant but add a bit of length; there is some redundancy between the opening statement and the 'question' parameter explanation, though overall it remains efficient.

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

Completeness3/5

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

Given the tool's interactive Q&A nature, the description covers purpose, prerequisites, and parameter sourcing, which is sufficient for basic use. However, it omits details about possible errors (e.g., if Ollama is not running), output format, or expected response behavior, leaving some gaps for an agent invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Since the schema has no field descriptions, the description carries the burden. It explains that 'question' is a natural-language query and that 'domain' comes from list_domains() and is auto-detected if omitted. This adds meaning, but it still lacks examples, constraints, or details about expected input format, leaving some ambiguity.

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: 'Ask a natural-language question answered by Qwen grounded on the NVIDIA CKG.' This includes a specific verb, object, and resource, and it distinguishes the tool from siblings like search_concepts or query_ckg by emphasizing the natural-language Qwen-based interface.

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?

The description provides setup prerequisites and parameter guidance (e.g., domain from list_domains(), auto-detection), but it does not explicitly state when to use this tool versus alternatives, nor does it mention situations where another sibling tool would be more appropriate. The intended use case is implied but not directly stated.

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

Deploy Server