An MCP server providing semantic data search using embeddings and similarity matching. Facilitates AI-powered, context-aware data retrieval for development teams.
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MCP server for accessing Tavily AI search API, offering advanced search within the MCP framework.
Category: Data Access Integration MCP Servers
Tags: mcp, semantic-search, vector-database, ai-integration, data-access
mcp-vector-search is a Model Context Protocol (MCP) server that enables semantic data search using vector embeddings and similarity matching. It facilitates AI-powered, context-aware data retrieval, specifically designed for querying transcript segments stored in a Turso database. Users can ask questions and receive relevant transcript segments, with results ranked by semantic similarity.
search_embeddings for semantic searchsearch_embeddings
question (string, required): Query textlimit (number, optional): Number of resultsmin_score (number, optional): Minimum similarityExpects a Turso database with two tables:
embeddings: id, transcript_id, embedding (vector)transcripts: id, episode_title, segment_text, start_time, end_timeGitHub: spences10/mcp-embedding-search
No pricing information provided; open source under MIT License.