"How to fetch or scrape data from a website for use in training an LLM" 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.

An authless Cloudflare Workers server that provides tools to scrape, analyze, and answer questions about websites using Cloudflare's Browser Rendering and AI capabilities.

Enables AI assistants to fetch and process web content securely, including HTML-to-markdown conversion, reader mode, metadata extraction, RSS/sitemap parsing, and robots.txt-aware requests with SSRF protection.

Let Claude Code, Codex and any AI agent use the Chrome you're already signed in to. Open-source CLI + MCP server + Chrome extension (native messaging, no debug port); stealth by default.

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 AI agents to autonomously operate web browsers through the Model Context Protocol, including navigating pages, clicking, typing, filling forms, and extracting structured data.

A personal MCP bridge that lets ChatGPT drive a local Codex CLI, translating instructions into Codex app-server threads and returning compact summaries of what Codex did.

Provides an MCP server for macOS that allows agents to control a managed browser with accessibility-first actions, visible cursor, and screenshot return.

Enables users to describe an LLM fine-tuning workload once and receive ranked, task-aware GPU recommendations across a dozen cloud providers based on required VRAM/FLOPs, cost, and runtime. It surfaces the cheapest, fastest, and balanced options through MCP so coding assistants can plan training hardware without manual price comparison.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

A thin MCP server that delegates lightweight tasks from Claude Code or any MCP-compatible client to local or cloud LLMs via LiteLLM, supporting models like Ollama and cloud APIs as subagents.

A local MCP gateway that unifies multiple LLM providers, API keys, routing, and multi-model discussions into a single tool surface.

Blind multi-model councils with anonymous LLM seats and direct 1:1 chat as a local MCP server. Enables asking one question to get independent answers from multiple model CLIs, then blind scoring and reveal.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Routes your AI tasks to the best available model across 20+ providers — automatically selecting based on task type, budget, and subscription pressure. Supports text, image, video, and audio with built-in cost optimization and fallback chains.

A Model Context Protocol (MCP) gateway for running Claude Code, Codex, Gemini, Grok, and Mistral (Vibe) CLIs from one MCP endpoint, with durable async jobs, session continuity, cache-aware prompting, observability, and personal-appliance setup tooling. Why developers try it: one local MCP endpoint for cross-LLM validation, multi-agent coding workflows.

Enables agents and users to route prompts to the cheapest reachable free or paid model across providers, compare costs, list filtered models, and receive fallback chains and cached routing results without calling any LLM API.

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.

MCP server that provides a live coordination layer for AI agents, including attributable handoffs, a shared event ledger, atomic work-claiming, and advisory file leases to prevent collisions.

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.

MCP server for the LLM Wiki Chrome extension that exposes bounded browser automation tools (shared tabs, accessibility snapshots, screenshots, clicks, typing, etc.) with explicit user consent and strict typing, enabling local agents to interact with specific web pages via a native messaging relay.

Enables an AI to delegate work to secondary Antigravity CLI accounts, running jobs in parallel with dependency (DAG) scheduling, file read/write declarations, warm multi-turn sessions, account quota tracking and compact result collection. Users simply describe their request in natural language while the AI plans and the server executes — no hand-written workflow JSON required.

An MCP server that routes LLM requests across multiple providers and orchestrates other MCP servers, with a focus on local privacy for embeddings and memory.

A small MCP server that lets Claude (or any MCP client) ask other models what they think.

Wraps Tool and ToolPackage instances from the llm-chat ecosystem behind the Model Context Protocol, enabling stdio and Streamable HTTP transports for MCP servers.

Enables AI agents to control and automate browser actions through an MCP server deployed on Heroku. Supports programmatic browser automation with natural language tasks across multiple LLM providers.

Enables MCP agents to drive the user's already-open Chrome profile through a Manifest V3 extension and loopback bridge, listing and controlling existing tabs, capturing screenshots, and sending clicks, typing, drags and key holds via an independent on-page cursor without moving the system mouse. It also supports form filling, console and network inspection, read-only or confirmed JavaScript execution, file and PDF downloads, and game-style screenshot/action loops with snapshot recovery and an emergency stop.

Enables Claude Code to control Firefox through tools for tabs, navigation, clicks, typing, scrolling, screenshots, page reading, forms, JavaScript, and console/network logs.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

TEX is an MCP server that enables Claude Code to perform browser tasks using plain language, driving a real browser to interact with web applications that lack APIs.