"How to fetch or scrape data from a website for use in training an LLM" matching MCP servers:

It enables AI agents to call a small set of reliable, permission-aware tools generated from an existing website through the Model Context Protocol, without rebuilding the site.

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

Enables agents to discover how a website serves its data, verify sources with evidence, export OpenAPI 3.1, and generate tested TypeScript or Python clients.

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.

An MCP server that enforces safe git commits by allowing only specified files and providing fixup capabilities for earlier commits.

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 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.

Lets MCP clients delegate private or bulk text tasks to a local OpenAI-compatible LLM while keeping data on the machine.

Enables MCP clients to turn Make blueprints and Zapier exports into n8n workflow files, with a per-step report of what converted, what to review, and what must be finished by hand. Also lists zaps, explains how individual steps are handled, checks workflow files for broken links or placeholders, and can upload the result to an n8n instance as an inactive workflow.

Give your AI assistant real-time LLM/VLM knowledge. Pricing, benchmarks, and recommendations — updated every hour, not every training cycle.

An MCP server that lets an AI agent use a real terminal the way a person does: type, press keys, click, read the screen, take a screenshot.

Runtime access for AI coding agents to observe and control Android and iOS apps.

MCP server that recommends which npm/PyPI libraries to use or avoid, with concise verdicts, alternatives, and code snippets, keeping responses under 500 tokens.

Lets Claude Code send prompts to a locally running Qwen model on Apple Silicon and get answers back, with optional thinking mode for harder problems. It also lists the registered models and reports whether each one is running.

Enables a primary agent to offload bounded, high-volume analysis of explicitly selected workspace files to an auxiliary LLM through a single read-only tool, returning text, Markdown, JSON, or unified-diff output with token usage while retaining all planning, verification, and edits.

Enables coding agents to query the current free LLM model lineup mid-task, check what free models were added or removed since the last scan, and look up curated provider free tiers with limits and caveats, so they stop hard-coding model IDs that silently break.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

Provides a universal bridge to interact with any OpenAI-compatible LLM API (local or cloud), enabling model testing, benchmarking, quality evaluation, and chat operations with performance metrics.

Enables coding agents to observe and control an authorized Android phone over ADB through MCP tools and a JSON CLI. It supports UI inspection, screenshots, taps, swipes, typing, key presses, app listing, and launching.

This MCP server enables MCP clients such as Claude Desktop to run LLM-generated code in a secure, isolated sandbox across multiple container backends and programming languages.

Enables coding agents to control a locally connected Android phone or emulator over ADB through MCP tools for device setup, UI inspection, taps, swipes, Unicode text input, app launching, and bounded batch actions, plus a live screen sidebar with clipboard capture.

An MCP server for automated website generation that transforms Excel spreadsheets and structured data into responsive portfolios, catalogs, and directories. It enables users to create complete websites, add pages, and generate components through natural language and template-based integration.

Enables AI agents to interact with multiple LLM providers (OpenAI, Anthropic, Google, DeepSeek) through a standardized interface, making it easy to switch between models or use multiple models in the same application.

Enables coding agents to inspect Python applications, research source and target models, and produce reviewable migration plans with evidence gates and evaluation before production code changes.

Share code context with LLMs via MCP or clipboard

A local-first memory cartridge for MCP-compatible AI clients, providing durable, inspectable, and auditable memory storage with a read-only-by-default MCP server.

Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.