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

An MCP server that connects AI assistants to YNAB budgets, enabling natural language queries about finances backed by full API coverage and built-in YNAB methodology knowledge.

Enables MCP clients to query a modular RAG knowledge hub with hybrid dense/sparse retrieval, reranking, multimodal image support, and traceable observability, all through natural language tools.

Enables video indexing with TwelveLabs by uploading local video files and tracking task status.

Automatically discovers database schema, performs data quality checks on tables and columns, and generates natural-language root cause analysis reports using Ollama LLM.

Enables document-based Q&A with multi-modal RAG, hybrid retrieval, knowledge graph reasoning, and multi-agent orchestration via MCP tools.

Enables managing a social media calendar workflow with AI-generated images/videos, including generating media, uploading to Google Drive, and downloading the latest calendar draft.

Enables EU AI Act Article 50(2) compliant watermarking of AI-generated content using C2PA manifest envelopes.

Enables AI-to-AI consultation for critical thinking and complex reasoning via OpenRouter, allowing one AI to delegate tasks to another AI model.

Enables AI assistants to perform retrieval-augmented generation with hybrid search, reranking, multi-modal image processing, and RAG evaluation through standardized MCP tools.

Enables AI clients to retrieve study-abroad admissions knowledge through a local, pluggable RAG pipeline with hybrid search, reranking, citations, and observability. It provides MCP tools, document ingestion, and a Streamlit dashboard for managing and evaluating retrieval.

Detects AI-generated content by analyzing 24 writing patterns, provides style analysis, plagiarism check, text humanization suggestions, and offers batch detection and version comparison tools.

Enables creating Manim animations via natural language commands using DeepSeek API, with a RAG system for Manim documentation retrieval.

The MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.

A lightweight RAG server deployed on Azure Container Apps that ingests PDFs, creates embeddings, and exposes a search_documents tool over MCP for retrieving relevant document chunks.

Generates RAG pipeline configurations from natural language descriptions, providing recommendations for chunking, embeddings, and vector stores.

Translates natural language commands into structured geometric operations for a simulated CAD engine using LLMs. It enables users to create shapes, modify dimensions, and perform 3D operations like extrusion through the Model Context Protocol.

Enables LLM clients to chat with locally indexed markdown engineering documentation through semantic search, keeping all indexing and retrieval on-device for privacy.

MCP server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

Enables a single MCP server for interacting with multiple vector databases through pluggable backends, starting with Pinecone. It supports schema-aware index management, upserts, hybrid search, embeddings, TTL conventions, and namespace operations.