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Glama

NVIDIA AI CKG

by io.github.Yarmoluk

verify_source

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
NameRequiredDescriptionDefault
domainYes
conceptYes

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

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and performs well: it exposes the audit chain, states that concept supports partial matches, and provides a concrete curl command for manual verification. It does not disclose failure modes, but the core behavior and expected outputs are clearly specified.

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 compact and well-structured: a one-sentence purpose, the audit chain, a verification command, and an Args block. Every line adds distinct value with no redundancy, and the most important information is front-loaded.

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 that an output schema exists, return values are already covered elsewhere. The description supplies the audit chain, verification method, and parameter sourcing, making it quite complete for a two-parameter tool. It lacks explicit when-not-to-use guidance and error behavior, but those are minor gaps here.

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

Parameters5/5

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

Schema description coverage is 0%, yet the Args section compensates fully by defining both parameters. It adds partial-match behavior for concept and directs the agent to list_domains() for valid domain values, including concrete examples like 'nvidia-nim' and 'nvidia-tensorrt-triton'.

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 opens with a precise statement: 'Return the source URL and SHA-256 content hash for any NVIDIA AI concept node.' This clearly identifies the verb, resource, and scope, and distinguishes verify_source from sibling query/search tools by focusing on provenance verification.

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 audit chain and verification command provide clear operational context, and the Args section explicitly instructs that domain should come from list_domains(), giving a concrete usage prerequisite. It does not explicitly name alternatives or exclusion criteria, but the verification purpose is evident enough.

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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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.

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