Activate vector search capabilities in Meilisearch to enable semantic similarity-based document retrieval using AI embeddings.
MIT
253,062 tools. Last updated 2026-06-30 19:42
"Understanding Vector Search" matching MCP tools:
- Get a list of Vector Search endpoints from your Databricks workspace.MIT
- Activate vector search functionality in Meilisearch to enable semantic similarity-based document retrieval.
- Apache 2.0
- Search a Qdrant collection by providing a raw vector to find similar items based on vector similarity, without needing a text query.Apache 2.0
- Search Meilisearch indexes using vector embeddings to find semantically similar content, supporting hybrid text-vector searches and customizable filtering.MIT
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- Flicense-qualityDmaintenanceCombines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.Last updated3
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Matching MCP Connectors
MDN Search MCP
x402-gated web search gateway. Tools: search, search_enriched.
- Search for similar items in a Meilisearch index using vector embeddings to find content based on semantic similarity rather than exact text matches.
- Query a Databricks Vector Search index using text or vector input to retrieve similar results with optional filters and scoring.MIT
- Search OpenMetadata entities using natural language queries with vector-based semantic matching.MIT
- Search stored information using natural language queries to find semantically similar content in vector databases.
- Search codebases by meaning using hybrid vector and graph analysis. Identify features like user authentication without exact keyword matches.MIT
- Log time-stamped events such as ideas, notes, or tasks, and index them for vector search to build a chronological memory of occurrences.AGPL 3.0
- Search your personal memory layers by combining vector similarity with keyword matching to retrieve relevant episodic, semantic, and procedural memories.AGPL 3.0
- Filter and retrieve Manticore Search documentation files using regex patterns to efficiently find multiple topics in a single call.Apache 2.0
- Retrieve the k nearest nodes to a query embedding using HNSW vector similarity. Use for semantic search after creating a vector index on a specific label and property.Apache 2.0
- Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.MIT
- Performs semantic vector search, then expands each result's graph neighborhood to reveal contextual relationships.Apache 2.0
- Search digital assets using vector or keyword queries. Use natural language filters to locate specific assets.ISC
- Search for similar documents by vector similarity, with optional scalar filters like age > 25.Apache 2.0
- Search Zvec vector stores by converting natural language queries into embeddings and retrieving similar items from a specified collection.Apache 2.0