NVIDIA AI CKG MCP

List all 21 NVIDIA AI domains available in this knowledge graph.

{
  "type": "object",
  "title": "list_domainsArguments",
  "properties": {}
}

{
  "type": "object",
  "title": "list_domainsOutput",
  "required": [
    "result"
  ],
  "properties": {
    "result": {
      "type": "string",
      "title": "Result"
    }
  }
}

Find concepts in a NVIDIA AI domain by keyword. Args: query: Search term — e.g. 'inference', 'sandbox', 'quantization', 'guardrails'. domain: Domain name from list_domains() — e.g. 'nvidia-nim', 'nvidia-openshell'.

{
  "type": "object",
  "title": "search_conceptsArguments",
  "required": [
    "query",
    "domain"
  ],
  "properties": {
    "query": {
      "type": "string",
      "title": "Query"
    },
    "domain": {
      "type": "string",
      "title": "Domain"
    }
  }
}

{
  "type": "object",
  "title": "search_conceptsOutput",
  "required": [
    "result"
  ],
  "properties": {
    "result": {
      "type": "string",
      "title": "Result"
    }
  }
}

Traverse the NVIDIA knowledge graph from a concept — prerequisites and dependents. Args: concept: Concept name (partial match supported) — e.g. 'TensorRT', 'NIM', 'Isaac Lab'. domain: Domain name from list_domains() — e.g. 'nvidia-tensorrt-triton', 'nvidia-isaac'. depth: Traversal depth 1–5 (default 3).

{
  "type": "object",
  "title": "query_ckgArguments",
  "required": [
    "concept",
    "domain"
  ],
  "properties": {
    "depth": {
      "type": "integer",
      "title": "Depth",
      "default": 3
    },
    "domain": {
      "type": "string",
      "title": "Domain"
    },
    "concept": {
      "type": "string",
      "title": "Concept"
    }
  }
}

Return the full ordered prerequisite chain for a concept — everything to learn first. Args: concept: Target concept — e.g. 'Speculative Decoding', 'Isaac Lab', 'NeMo Guardrails'. domain: Domain name from list_domains().

{
  "type": "object",
  "title": "get_prerequisitesArguments",
  "required": [
    "concept",
    "domain"
  ],
  "properties": {
    "domain": {
      "type": "string",
      "title": "Domain"
    },
    "concept": {
      "type": "string",
      "title": "Concept"
    }
  }
}

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.

{
  "type": "object",
  "title": "ask_nvidiaArguments",
  "required": [
    "question"
  ],
  "properties": {
    "domain": {
      "type": "string",
      "title": "Domain",
      "default": ""
    },
    "question": {
      "type": "string",
      "title": "Question"
    }
  }
}

{
  "type": "object",
  "title": "ask_nvidiaOutput",
  "required": [
    "result"
  ],
  "properties": {
    "result": {
      "type": "string",
      "title": "Result"
    }
  }
}

Discover other CKG packages for adjacent domains — finance, healthcare, legal, and more.

{
  "type": "object",
  "title": "list_ecosystemArguments",
  "properties": {}
}

{
  "type": "object",
  "title": "list_ecosystemOutput",
  "required": [
    "result"
  ],
  "properties": {
    "result": {
      "type": "string",
      "title": "Result"
    }
  }
}

Route an NVIDIA AI question to the optimal model and reasoning approach via graph depth. The CKG graph IS the router — hop depth is a deterministic complexity metric. Deeper NVIDIA prerequisite chains (CUDA → TensorRT → TensorRT-LLM → NIM) require more capable models. No heuristic: the graph decides. Routing table: hop_depth 1 → haiku · direct (simple lookup) hop_depth 2 → sonnet · generic_cot (moderate chain) hop_depth 3+ → opus · sparql_cot (deep dependency, structured reasoning) Args: question: Concept name or natural language question about NVIDIA AI. domain: Domain from list_domains() — e.g. "nvidia-tensorrt-triton", "nvidia-nim". Returns: model_tier + reasoning_approach + why + context subgraph to inject before LLM call.

{
  "type": "object",
  "title": "route_queryArguments",
  "required": [
    "question"
  ],
  "properties": {
    "domain": {
      "type": "string",
      "title": "Domain",
      "default": ""
    },
    "question": {
      "type": "string",
      "title": "Question"
    }
  }
}

{
  "type": "object",
  "title": "route_queryOutput",
  "required": [
    "result"
  ],
  "properties": {
    "result": {
      "type": "string",
      "title": "Result"
    }
  }
}

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'.

{
  "type": "object",
  "title": "verify_sourceArguments",
  "required": [
    "concept",
    "domain"
  ],
  "properties": {
    "domain": {
      "type": "string",
      "title": "Domain"
    },
    "concept": {
      "type": "string",
      "title": "Concept"
    }
  }
}

{
  "type": "object",
  "title": "verify_sourceOutput",
  "required": [
    "result"
  ],
  "properties": {
    "result": {
      "type": "string",
      "title": "Result"
    }
  }
}

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.

{
  "type": "object",
  "title": "query_intersectArguments",
  "required": [
    "branches",
    "domain"
  ],
  "properties": {
    "mode": {
      "type": "string",
      "title": "Mode",
      "default": "AND"
    },
    "depth": {
      "type": "integer",
      "title": "Depth",
      "default": 2
    },
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 40
    },
    "domain": {
      "type": "string",
      "title": "Domain"
    },
    "branches": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "title": "Branches"
    },
    "direction": {
      "type": "string",
      "title": "Direction",
      "default": "both"
    }
  }
}

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Build an interactive graph of the NVIDIA AI inference and robotics stack

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Map the full dependency chain for a production NVIDIA AI deployment

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Tell me what I need to get started with a specific NVIDIA technology

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All 21 NVIDIA AI domains — browsable index

ckg://domains

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Full CKG for a single NVIDIA AI domain — nodes, edges, metadata

ckg://nvidia/{domain}

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