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 — 65× 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	BERT F1	Tokens/query	Hallucination Rate
CKG	0.857	274	0%
RAG	0.817	17,900	Variable
GraphRAG	0.825	—	Variable

65× more efficient per token. Higher accuracy than both RAG and Microsoft GraphRAG. Zero hallucinations by construction.

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Install

pip install ckg-mcp

Claude Desktop Configuration

Add to your claude_desktop_config.json:

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

Works with Claude Desktop, LangGraph, AutoGen, or any MCP-compatible orchestrator.

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Tools

Tool	Description
list_domains()	List all 53 available CKG domains
query_ckg(domain, concept, depth)	Extract subgraph — prerequisites + dependents up to N hops
get_prerequisites(domain, concept)	Full prerequisite chain to root
search_concepts(domain, query)	Find concepts by name

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Example

# In your agent — via MCP tool call
query_ckg(domain="glp1-obesity", concept="Prior Authorization", depth=3)

# Returns the full causal chain:
## CKG: Prior Authorization (glp1-obesity)

### Prerequisites (what gates this)
  - Payer formulary tier assignment
    - Cost-effectiveness of GLP-1RA therapy
      - GLP-1 receptor agonist drug class
  - Medical necessity criteria

### Builds toward
  - Formulary position
  - Coverage decision

Same interface for codebases:

query_ckg(domain="langchain-core", concept="RunnableSequence", depth=2)
# Returns exact blast radius — 23 dependent modules — before your agent edits anything

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Bundled Domains (53 total)

Life Sciences & Clinical glp1-obesity · glp1-muscle-loss · drug-interactions · dementia · icd10-metabolic · modeling-healthcare-data · payer-formulary · cpt-em-coding · hipaa-compliance

Codebase & Software langchain-core · computer-science · circuits · digital-electronics · blockchain · quantum-computing · claude-skills

Mathematics & STEM calculus · algebra-1 · linear-algebra · pre-calc · geometry-course · chemistry · biology · ecology · genetics · bioinformatics · physics · signal-processing · fft-benchmarking

AI & Data machine-learning-textbook · data-science-course · conversational-ai · tracking-ai-course · prompt-class · intro-to-graph · systems-thinking · microsims

Business & Finance economics-course · personal-finance · organizational-analytics · it-management-graph · unicorns

Education & Other statistics-course · ethics-course · theory-of-knowledge · digital-citizenship · asl-book · reading-for-kindergarten · learning-linux · infographics · automating-instructional-design · functions · us-geography · moss

Enterprise domains (regulatory, legal, financial, custom) → graphifymd.com

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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, blast radius — that inference fails.

CKG is a pre-compiled routing layer: 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