About io.github.Yarmoluk/ckg-mcp
io.github.Yarmoluk/ckg-mcp is an MCP server in the RAG category: structured domain knowledge for AI agents. 42x more accurate than RAG, 11x fewer tokens.
Install
uvx ckg-mcppip install ckg-mcpThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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README
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.
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.
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.
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 |
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
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
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
License
MIT — Yarmoluk & McCreary, 2026. Commercial deployment → graphifymd.com
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.