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NVIDIA AI CKG

by io.github.Yarmoluk

query_intersect

Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B).

query_ckg walks outward from one concept. This intersects the reachable sets of two or
more, which is the shape of most real questions — "the component that satisfies A AND
applies to B". Neither anchor alone answers it; the answer lives in the overlap.

Every branch is an exact set of declared edges, so the intersection is exact. A concept
appears only if a declared path reaches it from each anchor. A relation missing from the
graph produces an empty result, never a guess.

Args:
    branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes
        everything within `depth` hops, or an anchor plus an explicit relation path using
        '>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the
        frontier. '*' matches any relation. Mix both forms freely.
    domain: Domain name from list_domains().
    depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths.
    direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default).
    mode: 'AND' (default) intersects branches; 'OR' unions them.
    limit: Max concepts listed, 1-200 (default 40). The true count is always shown.

Returns:
    Markdown with the query plan and its per-step set sizes, then the answer set with
    taxonomy tags. Reports which branch was empty when the intersection is empty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoAND
depthNo
limitNo
domainYes
branchesYes
directionNoboth

Schema Changelog

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

  1. Added

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden, and it does so thoroughly. It discloses that the intersection is exact, that missing relations yield an empty result rather than a guess, that limit shows the true count, and that the return value is Markdown with per-step set sizes and taxonomy tags. It also reports which branch was empty when the intersection is empty, which is valuable runtime behavior.

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 well-organized with a clear purpose statement, a short rationale distinguishing it from query_ckg, an Args block mapping to parameters, and a Returns block. Each sentence adds information about semantics, edge cases, or output behavior. It is longer than average, but the tool is complex and the length is proportional to what an agent needs to invoke it correctly.

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

Completeness5/5

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

Given six parameters, zero schema descriptions, no output schema, and no annotations, the description is complete enough for correct invocation. It covers all parameters, explains return format, describes the empty-result behavior, and references list_domains() for obtaining the domain value. No critical operational detail is left to inference.

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%, so the description must compensate, and it does comprehensively. It explains branches with bare anchors versus explicit relation paths, the meaning of '>' and '*', that depth applies only to bare-anchor branches, the direction options, mode AND/OR semantics, and that limit caps listed concepts while still showing the true count. This goes well beyond the raw schema fields.

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 definition: 'Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B).' It names the specific operation, the resource (concepts in the graph), and explicitly contrasts itself with sibling query_ckg, which walks outward from one concept. An agent can readily distinguish this tool from its siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly identifies the alternative query_ckg and the condition that selects between them: query_ckg handles one concept, while query_intersect handles two or more anchors and finds the overlap. It also explains why this shape fits 'most real questions' and notes that neither anchor alone answers the query. This gives clear when-to-use versus when-not-to-use guidance.

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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