"Understanding Vector Search" matching MCP servers:

Enables full-text search of macOS iMessages including link preview metadata. Works as an MCP server for Claude Desktop to search your messages locally.

Downloads your entire Search Console dataset into a local SQLite database, then gives your LLM a pre-built SQL query library for every standard SEO analysis type, with context available for your LLM to perform any SQL query to answer your questions and analyse for you.

A Model Context Protocol server for reading TiDB RAG knowledge base, enabling browsing, searching, and vector search of knowledge entries.

A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.

Enables AI systems to perform full-text and semantic search operations over structured/unstructured data in Azure Cognitive Search, with capabilities for document indexing and management through natural language.

Provides access to Naver Search APIs, allowing AI agents to search across multiple categories (blogs, news, books, images, shopping items, etc.) with structured responses optimized for LLM consumption.

Enables AI agents to manage and search data in Redis using natural language. Supports hashes, lists, sets, sorted sets, streams, JSON, and vector search.

Enables searching for any text within a specified Algolia index through an MCP-compatible interface. It allows users to integrate Algolia search capabilities into their environment using an application ID and API key.

An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

MCP server that aggregates job listings from Remotive and Arbeitnow, with local SQLite caching, application tracking, and saved searches.

Enables LLMs to explore BigQuery datasets and tables, run safe read-only queries, and optionally perform vector search using BigQuery embeddings.

Implements long-term memory capabilities for AI assistants using PostgreSQL with pgvector for efficient vector similarity search, enabling semantic retrieval of stored information.

Enables AI agents to look up Chinese laws and statutory articles fully offline from a local SQLite database, resolving a law name plus article number to verbatim text, searching articles by keyword, and retrieving a law's chapter structure and version history. It also verifies citations in bulk, checking whether each quoted provision exists word-for-word and is currently in force.

MCP server with 32 tools for ETL ingestion, AI-generated data quality rules, AI transformations, vector search, and natural-language SQL. Works across Postgres, MongoDB, Kafka, S3/MinIO, HashiCorp Vault, and five vector stores (Qdrant, Weaviate, Milvus, Chroma, pgvector).

A server that enables document searching using Vertex AI with Gemini grounding, improving search results by grounding responses in private data stored in Vertex AI Datastore.

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.

Drop-in MCP server template with SQLite FTS5 search backend. ~300 lines, no vector DB, no embedding API, runs on a Pi.

This read-only MCP Server allows you to connect to Google Search data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp

Enables unified access to SQL, semantic vector search, and columnar analytics through a single MCP interface.

Enables agents to store, recall, and manage persistent memory documents through MCP tools, with authenticated context tokens and hybrid lexical/vector search.

Enables AI assistants to search, query, and manage Azure AI Search indexes using full-text, semantic, and vector search, alongside index and document operations.

Enables natural language querying of PostgreSQL via MCP, combining SQL, vector search, and knowledge graph with automatic routing and token-aware curation.

Universal MCP server for readonly-first access to Oracle, SQL Server, PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, and Qdrant vector search.

MCP server for interacting with MariaDB databases, supporting standard SQL operations and optional vector/embedding search.

Builds rich code graphs from TypeScript/NestJS codebases using AST analysis and Neo4j, enabling semantic search, natural language querying, and intelligent graph traversal to provide deep contextual understanding of code relationships and dependencies.

An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.

A Model Context Protocol server that searches transcript segments in a Turso database using vector similarity, allowing users to find relevant content by asking questions without generating new embeddings.

Enables natural language queries to be converted into policy-verified SQL, vector search, and knowledge graph plans, with evidence-backed answers and an audit log.

Enables interaction with VikingDB, a high-performance vector database, for storing and searching vector data via natural language.