F
licenseA
qualityC
maintenanceEnables agents to share state via a blackboard, record and retrieve failure resolutions, and cache LLM responses to avoid redundant API calls.
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genpark-agent-semantic-cache-similarity-deduplicator-skill
by Alpha-Park
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-skillcheck if we've already answered a similar question and reuse that response"
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
โก Overview & Architectural Significance
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.
๐ Key Architectural Capabilities
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: echo-cache
๐๏ธ Architectural Topology & State Machine
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"]๐ Quickstart & Standalone Execution
Local Python Client Usage
from client import AgentSemanticCacheDeduplicator
# Initialize engine
engine = AgentSemanticCacheDeduplicator()
# Execute self-testing benchmark suite
result = engine.run_benchmark_semantic_cache()
print("Execution Result:", result)๐ One-Click MCP Integration (Claude Desktop / Cursor)
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"]
}
}
}๐ฆ Smithery.ai & PyPI Deployment
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
Maintenance
ActivityMaintained
ResponsivenessNo issues
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- -licenseNot gradedqualityNot gradedmaintenanceSemantic caching MCP server for AI agent tool calls, providing exact and similarity-based cache lookup, store, invalidation, and metrics via MCP tools.-
- FlicenseNot gradedqualityBmaintenanceEnables agents to deduplicate semantic prompt caches by measuring Jaccard token overlap, preventing redundant LLM calls and reducing cost. It provides deterministic lookup-or-insert cache behavior through an MCP-compatible interface.8-
- FlicenseNot gradedqualityBmaintenanceEnables agents to deduplicate semantic prompt cache entries by calculating Jaccard token overlap, reducing redundant LLM calls and cost.7-
