Fetch MCP

Overview

Fetch‑MCP is a lightweight Model Context Protocol server that bridges AI assistants with the web by providing two core capabilities: URL content retrieval and YouTube transcript extraction. In many AI workflows, the assistant needs up‑to‑date information or specific video subtitles to answer questions accurately. Rather than hard‑coding web scraping logic into each client, Fetch‑MCP exposes a clean, versioned API that any MCP‑compatible assistant can call. This abstraction lets developers focus on higher‑level reasoning while the server handles network requests, error handling, and data formatting.

The server solves a common bottleneck: pulling external content into the AI’s context in a consistent, rate‑limited, and privacy‑aware manner. By encapsulating HTTP requests behind MCP tools, developers can avoid the pitfalls of raw network calls—such as handling redirects, parsing HTML, or dealing with API keys. For YouTube transcripts, the server automatically fetches and parses closed‑caption data, returning plain text that can be fed directly into the assistant’s prompt. This is especially valuable for content creators, researchers, or support bots that need to reference specific passages from videos.

Key features include:

• URL Fetch Tool – Accepts any public URL and returns the raw HTML or extracted text, optionally stripping boilerplate for cleaner input.
• YouTube Transcript Tool – Retrieves the transcript of a YouTube video by ID or link, handling language selection and subtitle availability.
• Built‑in Rate Limiting – Prevents abuse by throttling requests per client, ensuring fair usage and compliance with target site policies.
• Error Reporting – Provides structured error messages (e.g., 404, timeout) that the assistant can interpret and retry or fallback gracefully.
• Extensible Plugin Architecture – Future plugins (e.g., RSS feeds, API wrappers) can be added without changing the core protocol.

Typical use cases span from research assistants that need to pull recent news articles or academic PDFs, to customer support bots that fetch product pages and display up‑to‑date specs. In content creation, a video editor can prompt the assistant to summarize a tutorial by fetching its transcript and generating a concise outline. In education, an e‑learning platform can let students ask questions about a lecture video, with the assistant retrieving the transcript on demand.

Integration is straightforward: an MCP‑enabled assistant declares a tool with the capability, specifying the endpoint and expected parameters. When invoked, the assistant sends a JSON request to Fetch‑MCP; the server returns structured content that the assistant can embed in its response or use for further processing. Because the server adheres to the MCP spec, it works seamlessly with any client that understands tools—Claude, GPT‑4o, or custom agents built on LangChain. This decoupling of data retrieval from AI logic promotes modular, maintainable systems where updates to fetching strategies can be rolled out independently of the assistant code.

In summary, Fetch‑MCP provides a reliable, protocol‑driven bridge to web content and YouTube transcripts. Its focused feature set, robust error handling, and MCP compatibility make it an essential component for developers building AI applications that require dynamic, external information without embedding complex scraping logic into their models.