"Front-end MCP services related to Vue" matching MCP servers:

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 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.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Enables Claude Desktop and compatible agents to search, read, add, update, and delete a locally stored, user-curated memory using hybrid retrieval with reranking and Qdrant. All processing runs locally without cloud services or API keys.

Enables interaction with Moorcheh AI services including namespace management, document embedding, vector search, and AI-powered answers through the Model Context Protocol.

Enables making REST API calls to Teradata cloud services including Elastic Compute, Vector Store, OMS, and QueryGrid. Supports custom authentication, Socks5 proxy, and multipart file uploads.

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

An MCP server for OpenServerless that exposes action tools for creating, invoking, and managing API endpoints with integrated services like S3, PostgreSQL, Redis, and Milvus.

Enables AI agents to autonomously create and manage topic-specific vector knowledge bases with end-to-end functionality including project creation, content ingestion from URLs, semantic search, and progress tracking. Provides a complete research workflow without exposing low-level APIs.

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.

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.

Provides persistent memory management for AI agents with 14 tools, including semantic search, context synthesis, auto-context middleware, and write-back actions to external services like Slack.

Enables Claude to interact with core AWS services like S3, EC2, RDS, and CloudWatch, along with a generic SDK wrapper for any AWS operation. It also supports cost monitoring and optional vector store capabilities for document ingestion and search.

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.

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

A Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.

A Model Context Protocol server that enables users to perform third-party enrichment lookups for security observables (IP addresses, domains, URLs, emails) through services like VirusTotal, Shodan, and others.

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.

An example server that enables interaction with Alibaba Cloud's Lindorm multi-model NoSQL database, allowing applications to perform vector searches, full-text searches, and SQL operations through a unified interface.

Enables LLMs to interact with Rememberizer Vector Store for semantic search, document creation, deletion, and modification.

Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.

Allows AI models to query and interact with FalkorDB graph databases through the Model Context Protocol (MCP) specification.

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.

Enables KoboldCpp local models to index and semantically query private documents from multiple file formats via MCP, with privacy-first local processing and zero data leakage.

MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.

Enables users to index and retrieve local documents through semantic, BM25, or hybrid search, with tools for chunk reads and page images accessible from MCP clients such as Codex and ChatGPT web.

Enables vector search and document retrieval, document-to-Markdown extraction and chunking, and private deep research through configured pipelines.

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.

Enables MCP clients to search locally indexed text files, PDFs, and images using natural-language descriptions, and to index folders and inspect index statistics.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.