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., "@Semantic Cache Query Matcher Cache this query-response pair, then look up a similar prompt"
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.
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.
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.
Related MCP server: mcp-lance-db
python mcp_server.pycache_set: Store query and generated response.
cache_get: Lookup semantic match for an incoming prompt.
cache_stats: Return total entries, hits, and cache utilization.
This server cannot be deployed
Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.
Universal memory for AI agents and tools. Save, organize and search context anywhere.
Memory system for AI agents with semantic search. Store and recall memories with ease.
Shared knowledge cache for AI coding agents — reuse an answer once it exists.
