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-agent-semantic-cache-similarity-deduplicator-skill Check the semantic cache for similar queries before answering: what is MCP?"
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-agent-semantic-cache-similarity-deduplicator-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: Arda Vector Database 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 AgentSemanticCacheDeduplicator
# Initialize engine
engine = AgentSemanticCacheDeduplicator()
# Execute self-testing benchmark suite
result = engine.run_benchmark_semantic_cache()
print("Execution Result:", result)Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-agent-semantic-cache-similarity-deduplicator-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-agent-semantic-cache-similarity-deduplicator-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-agent-semantic-cache-similarity-deduplicator-skill.gitThis server cannot be deployed
Persistent semantic memory storage, associative recall, and recent memory index by namespace.
Shared knowledge cache for AI coding agents โ reuse an answer once it exists.
LLM caching proxy (x402 USDC on Base) - exact + semantic cache. Free health.
Hosted persistent memory with semantic search, importance and TTL for AI agents.
