NVIDIA NemoClaw CKG MCP

Answer a question about NVIDIA NemoClaw by traversing the knowledge graph. Covers: agent runtimes (OpenClaw/Hermes/Deep Agents), OpenShell platform, inference routing, network policy, security layers, deployment paths, progressive tool disclosure, managed MCP servers, snapshots, shields, FOX Blueprint, Nemotron 3 Ultra ecosystem, and platform support. Args: question: Your question about NemoClaw concepts or architecture.

{
  "type": "object",
  "title": "ask_nemoclawArguments",
  "required": [
    "question"
  ],
  "properties": {
    "question": {
      "type": "string",
      "title": "Question"
    }
  }
}

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

Return the typed subgraph around a NemoClaw concept. Args: concept: Exact or partial concept label (e.g. 'OpenClaw', 'NetworkPolicy', 'L7Proxy'). depth: Traversal hops (1–5, default 3).

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

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

Return the full upstream prerequisite chain for a NemoClaw concept. Useful for understanding what a concept depends on end-to-end. Args: concept: Exact or partial concept label.

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

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

Fuzzy search for NemoClaw concepts by name or keyword. Args: query: Partial name or keyword (e.g. 'policy', 'inference', 'agent').

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

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

List available domains in this CKG server.

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

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

Return the authoritative source URL and content hash for a NemoClaw concept node. Every node in the CKG was declared from a specific source document. This tool returns the source URL (where the node came from) and the SHA-256 hash of that document's bytes at extraction time. A hash mismatch on re-fetch means either the source has changed (stale edge — re-extract) or the graph was patched without re-fetching (silent edit — investigate). Audit chain: edge answer → graph commit → source_hash → source_url (fetch hint) Verification: curl -s <source_url> | sha256sum # compare output to source_hash Args: concept: Exact or partial concept label (e.g. 'CorporateCA', 'L7Proxy').

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

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

Route a NemoClaw question to the optimal model and reasoning approach via graph depth. The CKG graph IS the router. NemoClaw's dependency chains (e.g. OpenShell → L7Proxy → CorporateCA → mTLS) are deep and typed — hop depth is a deterministic complexity signal. No heuristic: the graph decides which model and reasoning approach to use. Routing table: hop_depth 1 → haiku · direct (single concept lookup) hop_depth 2 → sonnet · generic_cot (moderate chain) hop_depth 3+ → opus · sparql_cot (deep chain, structured reasoning required) Args: question: Concept name or natural language question about NemoClaw / OpenShell. Returns: model_tier + reasoning_approach + why + context subgraph to inject before LLM call.

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

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

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