CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs

SSE

by Yarmoluk

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GitHub Website SSE

About

NVIDIA AI developer stack as a Compressed Knowledge Graph (CKG) — 20 domains, 998 nodes, every prerequisite chain declared as typed edges. Agents traverse REQUIRES/ENABLES relationships instead of scanning docs. 4× F1 vs RAG, 11× fewer tokens. No API key required

Details

Transport: SSE

Explore

- 4× F1 and 11× fewer tokens vs RAG
- 998 nodes across 20 NVIDIA AI domains
- Deterministic graph traversal – no hallucination
- Read-only: never writes, mutates, or executes
- Typed edges (REQUIRES, ENABLES, RELATES_TO, IMPLEMENTS)
- Three-state confidence (high, null, low) for every edge

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

From the repository

Install with pip install ckg-nvidia-ai and run the MCP server via uvx ckg-nvidia-ai. Configure it in Claude Desktop, Claude Code, Cursor, or other MCP clients using the provided JSON snippets. Tools include list_domains(), search_concepts(), query_ckg(), get_prerequisites(), and ask_nvidia() (the last requires Ollama with qwen2.5:14b).

`list_domains`

List all 20 NVIDIA AI domains available in this knowledge graph.

`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'.

`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).

`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().

`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.

`list_ecosystem`

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

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "ckg-nvidia- ai - nvidia developer stack as a compressed knowledge graph  - 20 domains, 998 nodes, agents traverse type dependency graphs": {
            "nvidia-ai": {
                "command": "uvx",
                "args": [
                    "ckg-nvidia-ai"
                ]
            }
        }
    }
}

McpServers

{
    "nvidia-ai": {
        "command": "uvx",
        "args": [
            "ckg-nvidia-ai"
        ]
    }
}

NVIDIA AI Developer Stack as a Compressed Knowledge Graph

4× F1 · 11× fewer tokens · 998 nodes · deterministic traversal

Instead of scanning docs or re-inferring structure on every query, your agent traverses declared relationships. 20 NVIDIA AI domains, every prerequisite chain typed and ready.

Install:
uvx ckg-nvidia-ai

Example:
query_ckg("TensorRT-LLM", "nvidia-tensorrt-triton", depth=3)

Returns: CUDA Toolkit, FP8/FP4 Quantization, Hopper SM90 as hard prerequisites. Triton Inference Server and NIM Microservice Runtime as dependents. 269 tokens. No hallucination.

If an edge isn't declared, the traversal returns nothing rather than hallucinating a path. That silence is signal.

Tools: list_domains · query_ckg · get_prerequisites · search_concepts · ask_nvidia (Ollama, no API key)

Domains: NIM · NeMo · TensorRT · CUDA · Isaac · Cosmos · Omniverse · Riva · Jetson · DRIVE · Clara · Metropolis · GameWorks · HPC SDK · CUDA-X · Developer Tools · Graphics Research · AI Enterprise · Developer Ecosystem · OpenShell

Benchmark: CKG F1 0.471 vs RAG 0.123 · 269 tokens vs 2,982 per query

github.com/Yarmoluk/ckg-nvidia-ai

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About

Details

Explore

Setting up with Highlight

From the repository

list_domains

search_concepts

query_ckg

get_prerequisites

ask_nvidia

list_ecosystem

Claude Desktop / Cursor

McpServers

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`list_domains`

`search_concepts`

`query_ckg`

`get_prerequisites`

`ask_nvidia`

`list_ecosystem`