"Vector Database" matching MCP servers:

Enables Claude to scrape, crawl, extract data, and sync to vector databases using scrapedatshi's RAG pipeline, with support for multiple embedding and vector DB providers.

Enables management of culinary recipes through web scraping from sites like Marmiton.org, recipe generation, ingredient management, and querying a MongoDB database of recipes, comments, users, and utensils.

An MCP server that connects language models to the LSD database, enabling web data extraction, research capabilities, and custom 'trips' for extending functionality through the internetdata SDK.

Captures Chrome DevTools Protocol messages, UI events, and web traffic into a local SQLite database, enabling querying and analysis via CLI or MCP tools.

Collects and stores Danbooru character data in a Turso cloud database with automatic pagination and upsert functionality for managing anime character post information.

Enables comprehensive LinkedIn profile search, data extraction, and contact enrichment using Google Search, Apollo.io, and AI-powered analysis. Includes automated data mining workflows with database storage and CSV export capabilities.

Provides web crawling and RAG capabilities for AI agents, enabling scraping of websites, storing content in a vector database (Supabase), and performing semantic search over crawled data.

A local vector database RAG system that integrates with Playwright MCP for web scraping, enabling users to build searchable knowledge bases from web content with multiple embedding providers and Claude-optimized context formatting.

Provides AI agents with a comprehensive web intelligence stack including crawling, private search via SearXNG, and intelligent RAG capabilities for focused content extraction. It supports advanced features like semantic vector search and knowledge graph integration for code validation to enhance AI performance and reliability.

Enables users to scrape and index documentation and query it through MCP, a CLI, and a web UI using local multilingual vector embeddings, with no API keys and offline operation after setup.

Enables AI agents to search and retrieve Yad2 real estate listings (for rent or sale) using MCP tools, with data scraped via GitHub Actions and served from a Cloudflare D1 database.

Crawls and indexes documentation websites to Supabase with vector embeddings for RAG, using smart sitemap discovery and Jina AI for fast content extraction with multi-project support.

An MCP server that integrates Crawl4AI with Supabase to enable AI agents to crawl websites, store content in a vector database, and perform RAG queries.

Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.

Crawls documentation websites and provides semantic search capabilities over the content through vector embeddings, enabling natural language queries of technical documentation.

A database-agnostic MCP server that integrates web crawling with Retrieval Augmented Generation, supporting multiple AI providers and vector database backends for flexible and intelligent content retrieval.

Enables AI agents to search the web, crawl websites, and perform intelligent RAG queries with semantic search capabilities. Includes integrated private search engine, vector database storage, and optional knowledge graph for AI hallucination detection in code repositories.

Open-source MCP servers for ESG data extraction, analysis, and regulation management, providing 31 tools across 6 servers for tasks like metrics extraction, PDF processing, vector storage, and web scraping.

An MCP server for searching and managing Tamil movies from moviesda16.com using a local SQLite database. It enables users to search for movie titles, manually add listings, and automatically update the database through web scraping.

Enables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.

MCP server for crawling web documentation and storing it in a Supabase vector database.

Monitors product prices by comparing database reference prices with real-time G-Market prices and sends Slack notifications when prices drop.

Enables AI assistants to crawl websites, extract and store web content with semantic search capabilities using vector embeddings, and retrieve information through natural language queries with tag-based filtering and intelligent content cleaning.

Scrapes and serves India's national vehicle registration database (VAHAN Dashboard), providing detailed insights into registrations, manufacturers, fuel types, and RTO-level metrics.

Creates deterministic, auditable vector databases from any content source with deployable RAG applications. Supports multiple embedding providers and vector databases with fine-grained pipeline control or project-based workflows.

Enables querying a vector database of machine learning FAQs and performing web searches using Bright Data proxies through Claude Desktop with structured agent prompts.

A multi-agent financial portfolio risk analyzer that provides MCP tools for fetching market news and storing compliance-reviewed assessment reports in a local database.

An MCP server for searching the Jurisprudencia de Buenos Aires (JUBA) database for legal summaries and full-text judicial rulings. It enables AI assistants to perform advanced searches across various legal matters from the Supreme Court of the Province of Buenos Aires.

Enables searching and scraping job postings from supported sites via Playwright, extracting details, registering them to a Notion database with deduplication, and sending tiered collection summaries to Discord.

LinkedIn prospect intelligence inside Claude. It automates the research behind outreach, not the sending: scrapes posts, profiles and comment threads into a local SQLite database, classifies what a person or company actually posts about, mines comment threads for people already describing your problem, and surfaces uncommon commonalities worth opening with.