"Information about extraction techniques or processes" matching MCP servers:

Enables Claude to extract text and metadata from PDF files via URL or base64 encoding. Supports optional bearer token authentication and can be deployed as a custom connector on claude.ai.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Enables AI assistants to search and ask questions about documents stored in AnythingLLM workspaces through MCP tools.

Enables AI assistants to search and retrieve transcripts from the Entra.Chat podcast about Microsoft Entra ID, with timestamped YouTube links and guest information.

Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.

Interact with WebScraping.AI API for web data extraction and scraping

Provides access to Jina AI's suite of web tools including URL reading, web/image/academic search, content extraction, embeddings, and reranking capabilities. Enables AI assistants to search the web, extract content from URLs, and process information with semantic understanding.

This server enables AI systems to integrate with Tavily's search and data extraction tools, providing real-time web information access and domain-specific searches.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

Give AI agents eyes on any web page: pixel-perfect screenshots, print-ready PDFs, branded OG images, code images, and structured page extraction (JSON or Markdown).

PDF extraction that actually works. The only extractor that audits every page. #2 on opendataloader-bench. 5 MCP tools for AI agents: metadata, convert, analyze, batch, structured extraction.

AgisHub provides a diverse set of pay-per-call tools for AI agents—covering timezones, web extraction, AI, memory, webhooks, finance, and utilities—accessible via a free MCP tier or x402 HTTP micropayments in USDC on Base, with no signup or API keys required.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

Enables AI agents to record, recall, correct, and forget evidence-backed factual claims with temporal history, while explaining whether remembered information is current, historical, or contested.

A Model Context Protocol server that allows AI agents to perform WHOIS lookups, enabling users to directly ask the AI about domain availability, ownership, registration details, and other domain information.

A Model Context Protocol server that provides access to Outscraper's data extraction services, implementing 8 tools for extracting data from Google services and domains including Maps, Reviews, Search, and contact information.

Provides LLMs with specialized knowledge about fountain pen inks, enabling intelligent ink search, color matching, and recommendations through various tools.

Provides persistent memory for AI agents, including context storage, facts, plans, RAG search, code snippets, and conversation compaction, enabling state to survive across sessions and processes.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

A Model Context Protocol server that intelligently fetches and processes web content, transforming websites and documentation into clean, structured markdown with nested URL crawling capabilities.

An MCP server for indexing and searching Commodore 64 documentation using full-text, semantic, and fuzzy search across multiple file formats. It enables RAG-based question answering, entity extraction, and interactive timeline or knowledge graph visualizations.

Enhances large language models with competitive programming knowledge by leveraging OI-Wiki content through vector search, allowing models to retrieve relevant algorithms and techniques.

Facilitates two-stage reasoning processes using DeepSeek for detailed analysis and supports multiple response models such as Claude 3.5 Sonnet and OpenRouter, maintaining conversation context and enhancing AI-driven interactions.

Enhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.

Enables web search functionality through Perplexity AI's API with time-based filtering options. Allows users to search the web for recent information within specified time periods (day, week, month, or year).

An automated system that processes raw text through a three-stage pipeline to assess data quality, enhance information, and generate structured knowledge graphs. It provides tools for triple extraction with confidence scoring and creates interactive HTML visualizations of the resulting graph.

Enables enterprise document retrieval using graph-based reasoning and knowledge graphs. Allows agents to search and extract information from scattered documents through structured entity and relationship extraction.

A systematic reasoning MCP server for Claude Desktop, featuring Beam Search and Monte Carlo Tree Search to facilitate complex problem-solving and decision-making processes.

IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real