ckg-mcp 0.1.1

Compact Knowledge Graph MCP server — 42× more efficient than RAG on structural queries

ckg-mcp

mcp-name: io.github.Yarmoluk/ckg-mcp

Compact Knowledge Graph MCP server. Pre-structured domain knowledge as a routing layer for agent stacks — 42× more efficient than RAG on structural queries.

Built on the CKG Benchmark — 45 domains, 7,928 queries, fully reproducible results.

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What It Does

Drop CKG into your agent stack as an MCP tool. Instead of retrieving text chunks and hoping the LLM infers structure, CKG gives agents pre-compiled dependency paths, prerequisite chains, and concept relationships — directly from a structured graph.

System	Macro F1	Tokens/query	Hallucination Rate
CKG	0.471	269	0%
RAG	0.123	2,982	Variable
GraphRAG	0.120	3,450	Variable

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Install

pip install ckg-mcp

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "ckg": {
      "command": "ckg-mcp"
    }
  }
}

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Tools

Tool	Description
list_domains()	List all available CKG domains
query_ckg(domain, concept, depth)	Extract subgraph — prerequisites + dependents
get_prerequisites(domain, concept)	Full prerequisite chain to root
search_concepts(domain, query)	Find concepts by name

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Bundled Domains (v0.1.0)

Domain	Concepts
calculus	105
algebra-1	80
chemistry	95
biology	88
linear-algebra	72
data-science-course	91
economics-course	78
glp1-obesity	90

More domains available via Graphify.md — weekly-updated commercial CKGs for clinical, regulatory, legal, and financial domains.

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Example

# In your agent — via MCP tool call
query_ckg(domain="calculus", concept="Taylor Series", depth=3)

# Returns:
## CKG: Taylor Series (calculus)

### Prerequisites (what you need to know first)
  - Power Series
    - Sequences and Series
      - Limits
  - Derivatives
  - Infinite Series

### Builds toward
  - Maclaurin Series
  - Error Estimation

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Why Not RAG?

RAG retrieves text chunks and forces the LLM to infer structure. On multi-hop structural queries (prerequisites, dependency chains, category aggregation), that inference fails — F1 = 0.123 vs CKG's 0.471.

CKG is a pre-compiled routing layer: the dependency paths are already in the graph. BFS/DFS traversal, not similarity search. No hallucinations by construction.

Full benchmark: github.com/Yarmoluk/ckg-benchmark

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License

MIT — Yarmoluk & McCreary, 2026. Commercial deployment → graphifymd.com