CKG-NVIDIA-AI
MCP server — Compressed Knowledge Graph (CKG) for the full NVIDIA AI developer stack.
4× F1 · 11× fewer tokens · 998 nodes · deterministic traversal.
Read-only
This MCP server never writes, mutates, or executes. Every response is a declared graph traversal — not inference, not retrieval, not generation.
The problem every AI team hits
More agents. More retrieval. More context. And accuracy drops.
This is the intelligence paradox: the more AI you add, the more tokens you burn re-discovering structure your system already knows — or could know. Research finds 73% of enterprise tokens are redundant context. In multi-agent pipelines, context efficiency collapses from 18.2 in Q1 to 1.6 by Q4 — 91% degradation with no model change.
The model is not the bottleneck. The context is.
Every time your agent calls out to ask what TensorRT-LLM requires to run on Hopper, it spends ~2,982 tokens re-inferring a relationship that could be declared once and traversed in 269. That difference compounds across every query, every agent, every boundary crossing.
The fix is not a better model. It is structured context.
What this package does
A CKG is a layer — a fast, inexpensive way to convert a large volume of domain documentation into structured, agent-traversable knowledge. Instead of retrieval, the agent traverses. Instead of inference, it reads declared relationships.
This package gives your agent the NVIDIA AI developer stack as its first layer: 20 domains, 998 nodes, every prerequisite chain declared and typed.
Layers stack. Context windows open.
| Layer | What your agent gains |
|---|---|
| NVIDIA AI docs (this package) | Platform prerequisites, deployment chains, hardware dependencies |
| Your domain | Company knowledge, internal APIs, product relationships |
| Competitive / market | Competitor stack, pricing, positioning — structured for traversal |
| Regulatory / compliance | Policy graphs, requirement chains, audit trails |
Each additional CKG layer costs your agent fewer tokens to operate, not more. Structured context is model augmentation — it does not replace what the model knows, it makes what it knows precise and auditable.
Explore the graph
Once installed, paste this into Claude, Cursor, or any MCP client:
You have access to the nvidia-ai CKG. I want to understand what it actually takes to deploy
a real-time speech AI pipeline on NVIDIA Jetson at the edge — not the marketing version,
the real dependency chain.
Start here:
get_prerequisites("Riva ASR", "nvidia-riva")
Then follow the chain into the inference layer:
query_ckg("TensorRT-LLM", "nvidia-tensorrt-triton", depth=3)
Then map the edge hardware:
get_prerequisites("Jetson Orin NX", "nvidia-jetson")
Present the result as a layered architecture map — foundation at the bottom, application at the
top, typed edges (REQUIRES / ENABLES) labeled between layers. Show where the three domains share
prerequisites. Flag anything that would block a cold-start deployment.
The graph will traverse four domains, surface shared prerequisites, and show you exactly what stands between an idea and a running system — no hallucination, no guessing, just declared relationships.
Quickstart
See the full Quickstart guide for MCP client setup.
Ecosystem
| Package | What it does |
|---|---|
| ckg-nvidia-ai | This repo — 20 NVIDIA AI domains, free |
| ckg-mcp | 97 domains: NVIDIA + science, finance, law, healthcare |
| agentmem-mcp | Cross-session agent memory, not vendor-locked |
| KRB Benchmark | Open benchmark dataset — reproduce the F1 numbers yourself |
graphifymd.com/pro/ — custom domain CKGs, sealed appliances, enterprise.
Patent pending.