Skip to main content

MCP Web Fetch

A Model Context Protocol server that provides web content fetching capabilities with robots.txt checking removed. This server enables LLMs to retrieve and process content from web pages, converting HTML to markdown for easier consumption.

This is a modified version of the original mcp-server-fetch that removes all robots.txt checking, allowing unrestricted access to web content.

[!CAUTION] This server can access local/internal IP addresses and may represent a security risk. Exercise caution when using this MCP server to ensure this does not expose any sensitive data. Additionally, this version ignores robots.txt restrictions which may violate some websites' access policies.

The fetch tool will truncate the response, but by using the start_index argument, you can specify where to start the content extraction. This lets models read a webpage in chunks, until they find the information they need.

Available Tools

  • fetch - Fetches a URL from the internet and extracts its contents as markdown.
    • url (string, required): URL to fetch
    • max_length (integer, optional): Maximum number of characters to return (default: 5000)
    • start_index (integer, optional): Start content from this character index (default: 0)
    • raw (boolean, optional): Get raw content without markdown conversion (default: false)

Available Prompts

  • fetch
    • Fetch a URL and extract its contents as markdown
    • Arguments:
      • url (string, required): URL to fetch

Installation and Usage

Local Development Setup

  1. Clone or download the source code:

    git clone <repository-url>
    cd mcp-web-fetch
    
  2. Install dependencies using uv:

    uv sync
    
  3. Test the server:

    uv run python -m mcp_server_fetch --help
    

Using with Claude Desktop (Local Source)

  1. Create Claude Desktop configuration:

    {
      "mcpServers": {
        "mcp-web-fetch": {
          "command": "uv",
          "args": [
            "run",
            "--directory",
            "/path/to/your/mcp-web-fetch",
            "python",
            "-m",
            "mcp_server_fetch"
          ]
        }
      }
    }
    
  2. Add configuration to Claude Desktop:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%/Claude/claude_desktop_config.json
    • Linux: ~/.config/Claude/claude_desktop_config.json
  3. Restart Claude Desktop to load the new server.

Using with VS Code (Local Source)

Add to your VS Code settings or .vscode/mcp.json:

{
  "mcp": {
    "servers": {
      "mcp-web-fetch": {
        "command": "uv",
        "args": [
          "run",
          "--directory",
          "/path/to/your/mcp-web-fetch",
          "python",
          "-m",
          "mcp_server_fetch"
        ]
      }
    }
  }
}

Installation via Package Manager

Using uv (recommended)

When using uv no specific installation is needed. We will use uvx to directly run mcp-web-fetch:

uvx mcp-web-fetch

Using pip

pip install mcp-web-fetch

After installation, run it as:

python -m mcp_server_fetch

Package Manager Configuration

Claude Desktop with uvx

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

VS Code with uvx

{
  "mcp": {
    "servers": {
      "mcp-web-fetch": {
        "command": "uvx",
        "args": ["mcp-web-fetch"]
      }
    }
  }
}

Development

Setting up Development Environment

  1. Install development dependencies:

    uv sync --dev
    
  2. Run linting and type checking:

    uv run ruff check
    uv run pyright
    
  3. Build the package:

    uv build
    

Testing

Test the server locally:

uv run python -m mcp_server_fetch

Use the MCP inspector for debugging:

npx @modelcontextprotocol/inspector uv run python -m mcp_server_fetch

Making Changes

  1. Edit the source code in src/mcp_server_fetch/
  2. Test your changes with uv run python -m mcp_server_fetch
  3. Update version in pyproject.toml if needed
  4. Run tests and linting

Publishing

Publishing to PyPI

  1. Build the package:

    uv build
    
  2. Publish to PyPI:

    uv publish
    

    Or using twine:

    pip install twine
    twine upload dist/*
    

Publishing to GitHub

  1. Initialize git repository (if not already done):

    git init
    git branch -m main
    
  2. Add and commit files:

    git add .
    git commit -m "Initial commit: MCP Web Fetch server without robots.txt checking"
    
  3. Create GitHub repository and push:

    # Create repository on GitHub first, then:
    git remote add origin https://github.com/langgpt/mcp-web-fetch.git
    git push -u origin main
    
  4. Create a release on GitHub:

    • Go to your repository on GitHub
    • Click "Releases" → "Create a new release"
    • Tag version: v0.6.3
    • Release title: v0.6.3 - MCP Web Fetch
    • Describe your changes
    • Publish release

Building Docker Image

docker build -t mcp-web-fetch .
docker tag mcp-web-fetch langgpt/mcp-web-fetch:latest
docker push langgpt/mcp-web-fetch:latest

Customization

robots.txt

This version has robots.txt checking completely removed. All web requests will proceed regardless of robots.txt restrictions.

User-agent

By default, depending on if the request came from the model (via a tool), or was user initiated (via a prompt), the server will use either the user-agent:

ModelContextProtocol/1.0 (Autonomous; +https://github.com/modelcontextprotocol/servers)

or:

ModelContextProtocol/1.0 (User-Specified; +https://github.com/modelcontextprotocol/servers)

This can be customized by adding the argument --user-agent=YourUserAgent to the args list in the configuration.

Proxy

The server can be configured to use a proxy by using the --proxy-url argument.

Debugging

You can use the MCP inspector to debug the server:

For local development:

npx @modelcontextprotocol/inspector uv run python -m mcp_server_fetch

For uvx installations:

npx @modelcontextprotocol/inspector uvx mcp-web-fetch

Contributing

This is a modified version of the original mcp-server-fetch. For contributing to the original project, see: https://github.com/modelcontextprotocol/servers

For this modified version, please submit issues and pull requests to this repository.

License

mcp-web-fetch is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

Metadata

Release files for mcp-web-fetch-langgpt 0.6.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mcp-web-fetch-langgpt 0.6.3
File Size Uploaded
mcp_web_fetch_langgpt-0.6.3.tar.gz 41.8 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for mcp-web-fetch-langgpt 0.6.3
File Interpreter ABI Platform
mcp_web_fetch_langgpt-0.6.3-py3-none-any.whl Python 3 none any Details

Total release size: 50.0 kB

Release files / mcp_web_fetch_langgpt-0.6.3.tar.gz

Download URL mcp_web_fetch_langgpt-0.6.3.tar.gz
Size 41.8 kB
Tags Source
SHA-256 checksum
How to use checksums
45064b31b7bd8a888a9ea9441ee6e8dbec838a7f8c84a09fd5dff2672cdaf80a
BLAKE2b-256 checksum
How to use checksums
b4bad9e00421874af1e5fd0d487ddf8f7069e17ea070bbc680754e179a03612c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.6.11

Release files / mcp_web_fetch_langgpt-0.6.3-py3-none-any.whl

Download URL mcp_web_fetch_langgpt-0.6.3-py3-none-any.whl
Size 8.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
ef2cf3aa3ae8db2d4d8715c78c717bcfbeffbfc870caddfb2900affe98c2457c
BLAKE2b-256 checksum
How to use checksums
420d6bdbd6047d81d1f8d4791f52392605cc5678df9db9bff8b75dd99e9ce325
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.6.11

Release history Release notifications | RSS feed

This release

0.6.3 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page