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8 clients · secrets stay placeholders

JSON (mcpServers)Claude CodeCodexOpenCodeCursorVS CodeGemini CLIGoose

{
  "mcpServers": {
    "ckg-nvidia-ai": {
      "type": "http",
      "url": "https://ckg-nvidia-ai.onrender.com/mcp"
    }
  }
}

claude mcp add --transport http ckg-nvidia-ai https://ckg-nvidia-ai.onrender.com/mcp

[mcp_servers.ckg-nvidia-ai]
url = "https://ckg-nvidia-ai.onrender.com/mcp"

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ckg-nvidia-ai": {
      "type": "remote",
      "url": "https://ckg-nvidia-ai.onrender.com/mcp",
      "enabled": true
    }
  }
}

{
  "mcpServers": {
    "ckg-nvidia-ai": {
      "type": "http",
      "url": "https://ckg-nvidia-ai.onrender.com/mcp"
    }
  },
  "deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=ckg-nvidia-ai&config=eyJ0eXBlIjoiaHR0cCIsInVybCI6Imh0dHBzOi8vY2tnLW52aWRpYS1haS5vbnJlbmRlci5jb20vbWNwIn0%3D"
}

{
  "servers": {
    "ckg-nvidia-ai": {
      "type": "http",
      "url": "https://ckg-nvidia-ai.onrender.com/mcp"
    }
  }
}

{
  "mcpServers": {
    "ckg-nvidia-ai": {
      "httpUrl": "https://ckg-nvidia-ai.onrender.com/mcp"
    }
  }
}

extensions:
  "ckg-nvidia-ai":
    enabled: true
    name: "ckg-nvidia-ai"
    type: streamable_http
    uri: "https://ckg-nvidia-ai.onrender.com/mcp"
    timeout: 300

Quality 76 / 100good

protocol75 · weight 25

warn
protocol.modernnewest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
n/a
protocol.statelessonly applies to 2026-07-28 servers
pass
protocol.transportstreamable HTTP
n/a
protocol.list_ttlonly applies to 2026-07-28 servers

authorizationn/a · weight 15

n/a
auth.prm, auth.as_metadata, auth.cimdno remote uses OAuth
n/a
auth.secret_in_urlno templated URL

tool hygiene64 · weight 30

pass
tools.descriptionsevery tool has a description
warn
tools.description_length1 tools have descriptions over 1,024 characters, which crowds the context
pass
tools.schemasevery tool has an object input schema
fail
tools.annotationsno tool declares readOnlyHint or destructiveHint, so clients cannot tell safe calls from risky ones
fail
tools.directory_hintsno tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: list_domains (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); query_ckg (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_prerequisites (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); …
pass
tools.token_costabout 1,543 tokens to load every tool
pass
tools.api_dumptools are not a one-to-one API dump

stabilityn/a · weight 15

n/a
stability.changes, stability.rug_pulltool history not recorded yet

dependencies100 · weight 15

pass
deps.known_vulnsno known advisory in 30 resolved packages
pass
deps.mcp_sdk_versionmcp 1.30.0, no known advisory
pass
deps.resolvable[email protected] resolved: 30 packages

9 tools, about 1,543 tokens to load them all · checked 2026-10-20 14:21 UTC

Tool changes

No tool definition changes recorded. A server's first observation is its baseline; later probes record what changes.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.

Trust 61 / 100

provenance80

liveness0

freshness85

hygiene100

+repository +namespace-matches-repo -unreachable(12 failures) ~updated-83d-ago

Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. How it is computed.

Dependencies

[email protected] PyPI29 dependencies, resolved · MCP SDK mcp 1.30.0 · advisories checked 2026-10-19

No known advisory affects this dependency graph.

Dependency graphs from deps.dev; advisories from OSV.dev, including the GitHub Advisory Database and the PyPI Advisory Database; all CC BY 4.0. The graph is what a clean install of this version resolves today; nothing was installed or run. How it is checked.

Endpoints

remote	auth	reachable	uptime 30d	p50	protocol	last ok
`https://ckg-nvidia-ai.onrender.com/mcp` streamable-http	none	no (12 failures)	48%	384 ms	2025-11-25	2026-09-22

package	registry	version	runtime	secrets
`ckg-nvidia-ai`	pypi	0.16.4	uvx

Tools (9)

`ask_nvidia`	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.
`get_prerequisites`	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().
`list_domains`	List all 20 NVIDIA AI domains available in this knowledge graph.
`list_ecosystem`	Discover other CKG packages for adjacent domains — finance, healthcare, legal, and more.
`query_ckg`	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).
`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.
`route_query`	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.
`search_concepts`	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'.
`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'.

Schemas: list_tools.

Similar MCP servers

More from io.github.Yarmoluk

Official server.json

{
  "name": "io.github.Yarmoluk/ckg-nvidia-ai",
  "title": "NVIDIA AI CKG",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "remotes": [
    {
      "url": "https://ckg-nvidia-ai.onrender.com/mcp",
      "type": "streamable-http"
    }
  ],
  "version": "0.16.4",
  "packages": [
    {
      "version": "0.16.4",
      "transport": {
        "type": "stdio"
      },
      "identifier": "ckg-nvidia-ai",
      "runtimeHint": "uvx",
      "registryType": "pypi"
    }
  ],
  "repository": {
    "url": "https://github.com/Yarmoluk/ckg-nvidia-ai",
    "source": "github"
  },
  "description": "NVIDIA AI knowledge graphs — 20 domains. 4x F1, 11x fewer tokens, SHA-256 provenance. MCP-native."
}