"Crypto Exchange MCP" matching MCP servers:

Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.

MCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.

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

Enables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

An MCP server for providing semantically related Ethereum Improvement Proposals (EIPs) to AI agents.

Enables vector similarity search and serving of Svelte documentation via the MCP protocol, with support for local caching and multiple llms.txt documentation formats.

Provides a plug-and-play persistent memory layer for MCP-compatible AI assistants, enabling them to store, retrieve, and delete memories across multiple databases simultaneously using semantic vector search.

Pinecone Assistant MCP server

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 Memory is a MCP Server that gives clients like Cursor and Claude the ability to remember user preferences and behaviors across conversations using vector search.

A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

MCP server for Milvus vector database enabling vector search, text search, and hybrid search operations.

Enables AI assistants to interact with MariaDB databases through standard SQL operations and advanced vector/embedding-based search. Supports database management, schema inspection, and semantic document storage and retrieval with multiple embedding providers.

A minimal RAG service that exposes a vector index for document retrieval via REST and MCP, allowing querying for relevant document chunks and returning a suggested LLM prompt.

Enables semantic search, recommendations, and similar crate lookups for Rust crates via vector embeddings.