"Connecting to Gemini Flash Experimental" matching MCP servers:

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

A dockerized Python MCP server that manages flash card projects using OpenAI embeddings and SQLite, enabling semantic search and storage of flash cards across projects.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

A Model Context Protocol server that provides access to Observe API functionality, enabling LLMs to execute OPAL queries, manage datasets/monitors, and leverage vector search for documentation and troubleshooting runbooks.

askDB is an MCP server that retrieves relevant database schema (DDL) from a Pinecone index and provides it to LLMs to write SQL, without connecting to the database itself.

Enables natural-language search over locally indexed files such as markdown, text, images, videos, and PDFs, and retrieves indexed text or media metadata by path. It lets Cursor query a local embedding index built with Gemini and SQLite.

MCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.

Provides Claude Code read-only access to PostgreSQL for relational queries, schema inspection, EXPLAIN plans, pgvector similarity searches, and Apache AGE graph traversals.

Enables passage-level semantic search over a Zotero library by extracting, chunking, and embedding PDF text using Gemini and ChromaDB. It provides MCP tools to perform topical searches and retrieve specific document passages with surrounding context.

Provides semantic search capabilities by connecting Claude Desktop to a Cloudflare Workers backend powered by Vectorize. It enables natural language querying of knowledge bases using vector similarity and edge-based embedding generation.

Model Context Protocol server for RosalindDB, enabling AI clients to create datasets, ingest vectors, run similarity queries, and check usage on a cost-optimized vector search database.

An MCP server that streams K-line (candlestick) data from decentralized exchanges (DEXs) to power your AI agents and workflows.

Persistent memory MCP server for AI coding agents (Claude Code, Codex, Gemini CLI). Hybrid retrieval (vector + BM25), cross-encoder reranking, knowledge graph, session checkpoint/resume, and multi-scope isolation. Local-first with LanceDB.

An experimental MCP server that makes YouTube playlist transcripts available for AI assistants, enabling search and conversation about video content.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

Enables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.

Connects AI clients to MindsDB via the MySQL protocol to execute SQL queries, manage databases, and perform semantic searches within knowledge bases. It enables automated workflows through job scheduling and provides seamless integration with external data sources.

A service discovery and proxy for MCP servers that enables registration, discovery, and execution of tools on remote MCP servers. Uses vector-based similarity search through Alibaba Cloud services to intelligently route requests to appropriate MCP services.