Fetch MCP Server logo

Fetch MCP Server

Community
zcaceres

A flexible HTTP fetching Model Context Protocol server.

Publisherzcaceres
Repositoryfetch-mcp
LanguageTypeScript
Forks
114
Stars
761
Available tools
4
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    Fetch MCP Server exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 4 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    761 stars and 114 forks from the linked repository.

Fetch MCP Server

fetch mcp logo

npm version

An MCP server for fetching web content in multiple formats — HTML, JSON, plain text, Markdown, readable article content, and YouTube transcripts.

Tools

All tools accept the following common parameters:

ParameterTypeRequiredDescription
urlstringYesURL to fetch
headersobjectNoCustom headers to include in the request
max_lengthnumberNoMaximum characters to return (default: 5000)
start_indexnumberNoStart from this character index (default: 0)
proxystringNoProxy URL (e.g. http://proxy:8080)
  • fetch_html — Fetch a website and return its raw HTML content.

  • fetch_markdown — Fetch a website and return its content converted to Markdown.

  • fetch_txt — Fetch a website and return plain text with HTML tags, scripts, and styles removed.

  • fetch_json — Fetch a URL and return the JSON response.

  • fetch_readable — Fetch a website and extract the main article content using Mozilla Readability, returned as Markdown. Strips navigation, ads, and boilerplate. Ideal for articles and blog posts.

  • fetch_youtube_transcript — Fetch a YouTube video's captions/transcript. Uses yt-dlp if available, otherwise extracts directly from the page. Accepts an additional lang parameter (default: "en") to select the caption language.

Installation

As an MCP server

Add to your MCP client configuration:

json
{
  "mcpServers": {
    "fetch": {
      "command": "npx",
      "args": ["mcp-fetch-server"]
    }
  }
}

As a CLI

bash
npx mcp-fetch <command> <url> [flags]

Or install globally:

bash
npm install -g mcp-fetch-server
mcp-fetch <command> <url> [flags]

CLI Usage

mcp-fetch <command> <url> [flags]

Commands

CommandDescription
htmlFetch a URL and return raw HTML
markdownFetch a URL and return Markdown
readableFetch a URL and return article content as Markdown (via Readability)
txtFetch a URL and return plain text
jsonFetch a URL and return JSON
youtubeFetch a YouTube video transcript

Flags

FlagDescription
--max-length <N>Maximum characters to return
--start-index <N>Start from this character index
--proxy <URL>Proxy URL
--lang <code>Language code for YouTube transcripts (default: en)
--helpShow help message
--versionShow version

Examples

bash
# Fetch a page as markdown
mcp-fetch markdown https://example.com

# Extract article content without boilerplate
mcp-fetch readable https://example.com/blog/post

# Get a YouTube transcript in Spanish
mcp-fetch youtube https://www.youtube.com/watch?v=dQw4w9WgXcQ --lang es

# Fetch with a length limit
mcp-fetch html https://example.com --max-length 10000

# Fetch through a proxy
mcp-fetch json https://api.example.com/data --proxy http://proxy:8080

Environment Variables

VariableDescription
DEFAULT_LIMITDefault character limit for responses (default: 5000, set to 0 for no limit)
MAX_RESPONSE_BYTESMaximum response body size in bytes (default: 10485760 / 10 MB)

Example with a custom limit:

json
{
  "mcpServers": {
    "fetch": {
      "command": "npx",
      "args": ["mcp-fetch-server"],
      "env": {
        "DEFAULT_LIMIT": "50000"
      }
    }
  }
}

Features

  • Fetch web content as HTML, JSON, plain text, or Markdown
  • Extract article content with Mozilla Readability (strips ads, nav, boilerplate)
  • Extract YouTube video transcripts (via yt-dlp or direct extraction)
  • Proxy support for requests behind firewalls
  • Pagination with max_length and start_index
  • Custom request headers
  • SSRF protection (blocks private/localhost addresses and DNS rebinding)
  • Response size limits to prevent memory exhaustion

Development

bash
bun install
bun run dev     # start with watch mode
bun test        # run tests
bun run build   # build for production

License

This project is licensed under the MIT License.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "zcaceres-fetch-mcp": {
      "command": "",
      "args": []
    }
  }
}

Use Fetch MCP Server MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Fetch MCP Server is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Fetch MCP Server as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Fetch MCP Server MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "zcaceres-fetch-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-fetch-server"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Fetch MCP Server MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Fetch MCP Server MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Fetch MCP Server to help me with this task?
Fetch MCP Server
Sure. I read it.
Here is what I found using Fetch MCP Server.

Frequently asked questions

What is the Fetch MCP Server MCP server used for?

Fetch MCP Server is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Fetch MCP Server MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Fetch MCP Server with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Fetch MCP Server MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Fetch MCP Server connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Fetch MCP Server MCP to TypingMind?

Fetch MCP Server runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Fetch MCP Server MCP provide in TypingMind?

Fetch MCP Server exposes 4 MCP tools that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Fetch MCP Server MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Fetch MCP Server requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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