"An MCP server for indexing S3 content into a RAG vector database" matching MCP servers:

Enables AI applications to run SQL queries and retrieve results from Oracle Database.

A local-only codebase indexer that extracts syntax-aware chunks, creates embeddings locally, and enables semantic code search and symbol lookup via MCP.

Enables managing and interacting with any S3-compatible storage endpoint through MCP tools for listing, reading, writing, copying, and deleting buckets and objects.

Enables agents to turn documents into vectors, perform approximate nearest neighbor search with HNSW, and filter by metadata via MCP tools for RAG workflows.

Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.

Enables file uploads to AWS S3 buckets with automatic MIME type detection.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Fireproof ledger database with multi-user sync

Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.

Enables LLM clients to safely inspect database schemas and run only approved read-only queries and stored procedures, with auditing.

A deterministic SVG perception tool for LLM agents that renders SVGs in a real browser to measure geometry, colors, and spatial constraints, reporting results in grid grammar and W3C color names without guessing.

An MCP server that indexes documents and serves relevant context to LLMs via Retrieval Augmented Generation (RAG).

MCP server for unified SQL database access, supporting multiple databases with schema discovery, caching, and safety controls.

A Model Context Protocol server for unified database access and management, supporting multiple databases with CRUD operations, transactions, metadata queries, and built-in security features.

A secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.

Enables indexing and semantic search of software project files via MCP, with automatic file watching for updates.

Provides local vector-based semantic memory storage for AI assistants to persist context and decisions across sessions using local embeddings and LanceDB. It enables private semantic search and session handoff capabilities to maintain long-term project context.

A Model Context Protocol (MCP) server for Retrieval-Augmented Generation (RAG) operations. It provides tools for building and querying vector-based knowledge bases from document collections, enabling semantic search and document retrieval capabilities.

A specialized MCP server for Claude Desktop that enhances AI-assisted medical learning by fetching and processing educational resources from PubMed, NCBI Bookshelf, and user-provided documents.

MCP RAG Server is a Python MCP server that indexes documents in multiple formats (Markdown, text, PowerPoint, PDF) using multilingual-e5-large embeddings and enables vector search for retrieval-augmented generation.