ckg-nvidia-ai

endpoint

https://ckg-nvidia-ai.onrender.com/mcp

door code

c4ff21f657b8d9e7

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is ckg-nvidia-ai live?

Yes — it answered the hub's last check (checked 21h ago). It answered 100% of checks over the last 30 days.

Is ckg-nvidia-ai free to use?

Not anonymously — it asks for a key or a login before it will serve. Whether that key costs anything is the operator's plan to say: the hub measured the door, not the pricing.

What tools does ckg-nvidia-ai have?

9 tools: query_intersect, route_query, search_concepts, ask_nvidia, verify_source, query_ckg, get_prerequisites, list_domains, ….

Is ckg-nvidia-ai safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• list_domains reads open 4d ago

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

  mcp-tool

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

  arguments 5 lines copy
• list_ecosystem reads open 4d ago

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

  mcp-tool

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

  arguments 5 lines copy
• query_intersect unknown 4d ago

  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.

  mcp-tool

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  {
    "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"
      }
    }
  }

  arguments 41 lines copy
• route_query unknown 4d ago

  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.

  mcp-tool

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

  arguments 18 lines copy
• search_concepts reads unknown 4d ago

  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'.

  mcp-tool

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

  arguments 18 lines copy
• ask_nvidia reads unknown never probed

  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.

  mcp-tool

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

  arguments 18 lines copy
• verify_source unknown never probed

  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'.

  mcp-tool

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

  arguments 18 lines copy
• query_ckg reads unknown 4d ago

  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).

  mcp-tool

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  {
    "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"
      }
    }
  }

  arguments 23 lines copy
• get_prerequisites reads unknown 4d ago

  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().

  mcp-tool

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

  arguments 18 lines copy

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Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.