"Creating Web Scraping Tools for Extracting Data from Websites" matching MCP servers:

An MCP server that exposes any REST API to LLMs through runtime discovery, providing tools to browse endpoints, fetch schemas, and apply business rules without hardcoding or schema duplication.

A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.

A fully working MCP server built from scratch in plain Node.js, implementing tools, resources, prompts, notifications, and sampling according to the MCP specification, designed to connect to Claude Desktop or any MCP client.

Enables to perform mathematical calculations, retrieve weather information, and manage files through a standardized MCP interface, showcasing best practices for building MCP servers.

Enables users to expose arbitrary HTTP APIs as configurable MCP tools by defining tool specifications in a JSON configuration file.

Enables clients to interact with a bundled documentation corpus through MCP tools and resources over HTTP or stdio, and provides a trajectory evaluation harness for grading agent performance.

A local MCP server that enables AI applications like Claude Desktop to securely access and work with Obsidian vaults, providing capabilities for reading notes, executing templates, and performing semantic searches.

A lightweight web scraping server that allows Claude Desktop users to extract various types of data from websites, including text, links, images, tables, headlines, and metadata using CSS selectors.

MCP server that exposes tools to route natural language queries to typed IDs, describe catalog entries, and list repository verification commands for the m-dev-tools organization.

Enables generating JSON Schema from sample data, generating TypeScript types, validating data against schemas, creating mock data from schemas, and comparing schemas for differences.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

MCP server that lets AI editors execute JavaScript, read console output, and simulate clicks/input on static web pages. Supports multiple browser pages and can be deployed remotely via HTTP transport.

Provides AI agents with data validation, transformation, and normalization capabilities, including JSON schema validation, CSV processing, data normalization, text cleaning, and dataset merging.

Enables AI agents to pay per call for live package-safety and GitHub merge-readiness trust signals via the x402 protocol.

Enables web performance analysis using Google PageSpeed Insights and Chrome UX Report APIs, providing compact metrics and diagnostics without local browser automation.

Provides computational tools for systematic reasoning, including boolean evaluation, date arithmetic, object counting, state tracking, and format validation, to enhance agent capabilities and eliminate calculation errors in reasoning tasks.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

Provides AI-powered tools to apply UX/UI best practices, Nielsen's heuristics, cognitive biases, and Material-UI patterns to React components. Enables automated application of responsive design, Apple design patterns, and complete UX guidelines through natural language commands.

Provides deterministic tools for transforming, formatting, and inspecting structured data for AI agents.

MCP server generated from an OpenAPI Specification, exposing tools via HTTP. It allows clients to invoke API operations using JSON-RPC 2.0 requests.