Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@genpark-knowledge-graph-entity-relation-triplet-extractor-skill extract entity-relation triples from 'Elon Musk founded SpaceX' and return JSON-LD"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-knowledge-graph-entity-relation-triplet-extractor-skill delivers zero-dependency, low-latency, deterministic agentic memory and retrieval primitives engineered strictly using Python 3.9+ standard library.
Zero External Dependencies: Operates exclusively via pure Python (math, re, collections, heapq, hashlib, json). Zero pip install overhead, zero C-extension compile errors.
Enterprise RAG & Memory Invariants: Implements formal algorithms for cognitive decay, BM25 Okapi lexical scoring, Reciprocal Rank Fusion, knowledge graph traversal, semantic query caching, and lost-in-the-middle context reordering.
Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
Related MCP server: Memsolus MCP Server
flowchart TD
UserQuery["User Prompt / Agent Goal"] --> SemCache["Semantic Cache Check"]
SemCache -->|Cache Hit| FastReturn["Cached Response (0ms)"]
SemCache -->|Cache Miss| DualRetrieval["Dual Retrieval Pipeline"]
subgraph DualRetrieval ["Hybrid Search Engine"]
BM25Lex["BM25 Okapi Lexical Ranker"]
DenseVec["Dense Cosine Vector Similarity"]
end
DualRetrieval --> RRF["Reciprocal Rank Fusion (RRF)"]
RRF --> GraphExp["Knowledge Graph Triplet Expansion"]
GraphExp --> LostMiddle["Lost-In-The-Middle Context Reorderer"]
LostMiddle --> LLM["LLM Synthesis with Optimal Context"]
LLM --> EpisodicMem["Episodic Consolidation & Recency Decay"]from client import KnowledgeGraphTripletExtractor
# Initialize engine
engine = KnowledgeGraphTripletExtractor()
# Execute self-testing benchmark suite
result = engine.run_benchmark_graph_extractor()
print("Execution Result:", result)Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-knowledge-graph-entity-relation-triplet-extractor-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-knowledge-graph-entity-relation-triplet-extractor-skill/mcp_server.py"]
}
}
}This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
pip install git+https://github.com/alphaparkinc/genpark-knowledge-graph-entity-relation-triplet-extractor-skill.gitThis server cannot be deployed
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
Shared long-term memory for AI agents: save and recall context as a searchable knowledge graph.
Cloud or self-hosted knowledge for AI agents: hybrid search, reranking, GraphRAG, scoped MCP tools.
Persistent knowledge graph for AI-augmented teams. Store decisions, findings, and standing rules across agent sessions with semantic search and typed connections. Includes cross-session memory, audit trail, workspace isolation, and secret detection. Built for teams running agents that need to remember. Free until launch with team tier as default, anon trial available.
