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Why this server?
This server provides AgentQL's data extraction capabilities enabling AI agents to get structured data from unstructured web
Why this server?
Provides functionality to fetch and transform web content in various formats (HTML, JSON, plain text, and Markdown) through simple API calls.
Why this server?
A Model Context Protocol (MCP) server implementation that integrates with FireCrawl for advanced web scraping capabilities.
Why this server?
An MCP server that provides real-time cryptocurrency news sourced from NewsData for AI agents.
Why this server?
Enables LLMs to autonomously retrieve and explore web content by fetching pages and recursively following links to a specified depth, particularly useful for learning about topics from documentation.
Why this server?
Extracts and transforms webpage content into clean, LLM-optimized Markdown. Returns article title, main content, excerpt, byline and site name. Uses Mozilla's Readability algorithm to remove ads, navigation, footers and non-essential elements while preserving the core content structure.
Why this server?
A Python server that enables interaction with Box files and folders through the Box API, allowing operations like file search, text extraction, and AI-based querying and data extraction.
Why this server?
A Model Context Protocol server that enables LLMs to interact with Elasticsearch clusters, allowing them to manage indices and execute search queries using natural language.
Why this server?
A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.