ckg-nvidia-ai

verify_source

Retrieve the source URL and SHA-256 content hash for any NVIDIA AI concept to verify its authenticity and audit trail.

Instructions

Return the source URL and SHA-256 content hash for any NVIDIA AI concept node.

Audit chain: edge answer → graph commit → source_content_hash → source_url (fetch hint).
Verification: curl -s <source_url> | sha256sum  # compare to source_hash

Args:
    concept: Concept label (partial match supported).
    domain:  Domain from list_domains() — e.g. 'nvidia-nim', 'nvidia-tensorrt-triton'.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`domain`	Yes
`concept`	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

A3.8/5.0

Behavior3/5

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

The description discloses the audit chain and verification method, which conveys read-only behavior and provenance transparency. However, it does not explicitly mention side effects, failure modes, or what happens when a concept or source is not found, leaving some behavioral details implicit.

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-structured, with three short sections covering purpose, audit chain, and parameters. Every line adds useful information with no redundant or filler content.

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 provides enough operational context, including the audit chain, verification command, and parameter guidance, for an agent to use the tool effectively. It does not explicitly contrast with sibling tools, but the verification-focused purpose is clear enough for most scenarios.

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 description adds meaning to both parameters: concept is described as a partial-match label, and domain is tied to list_domains() with examples. This compensates for the empty schema descriptions and gives an agent actionable parameter guidance.

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 primary function: returning the source URL and SHA-256 content hash for NVIDIA AI concept nodes. This is specific and unambiguous, making the tool's purpose immediately understandable.

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 verification instructions and an audit chain, but it does not explicitly state when to use this tool versus sibling tools like search_concepts or query_ckg. An agent must infer usage context from the name and sibling list rather than from direct guidance.

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

Deploy Server