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genpark-agent-semantic-cache-query-matcher-skill

mcp
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SUMMARY

Fast semantic query cache with fuzzy term similarity and canonical intent hashing to avoid duplicate LLM invocations and cache prompt responses safely

README.md

Semantic Cache Query Matcher Skill for Autonomous Agents

MCP Compatible
Python 3.9+
License: MIT
Zero External Dependencies

High-performance in-memory semantic cache designed to slash LLM inference costs and latency by caching query-response pairs with fuzzy cosine term-vector matching.

Key Features

  • Cosine Term Vector Matching: Zero external vector database; computes term-frequency vector similarity in pure Python.
  • Configurable Similarity Threshold: Tunable confidence threshold (e.g. 0.75 - 0.95) to prevent false-positive matches.
  • Hit Rate Telemetry: Tracks hits, misses, and saved token estimations.

MCP Server Integration

python mcp_server.py

Available MCP Tools

  • cache_set: Store query and generated response.
  • cache_get: Lookup semantic match for an incoming prompt.
  • cache_stats: Return total entries, hits, and cache utilization.

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