web-fetch-mcp
README.md
# Web Fetch MCP Server
[English](README.md) | [中文](README-zh.md)
[](https://badge.fury.io/js/web-fetch-mcp)
A Model Context Protocol (MCP) server that provides web content fetching, summarization, comparison, and extraction capabilities.
## Features
- **Three Core Tools**: Provides `summarize_web`, `compare_web`, and `extract_web` for versatile web content processing.
- **Handles Multiple URLs**: Process up to 20 URLs in a single request.
- **Content Transformation**: Converts HTML to clean, readable text and automatically resolves GitHub `/blob/` URLs to their raw content equivalent.
- **Safe & Secure**: Protects against Server-Side Request Forgery (SSRF) by blocking requests to private IP addresses.
- **Configurable**: Allows setting timeouts and content length limits to manage performance.
## Installation
Install the server globally from npm:
```bash
npm install -g web-fetch-mcp
```
## MCP Agent Configuration
To use this server with an AI agent that supports the Model Context Protocol, add the following configuration to your agent's settings. Once configured, your agent can call the tools provided by this service.
**Important:** You must provide a valid Gemini API key for the server to work.
If you installed the package globally:
```json
{
"mcpServers": {
"web-fetch-mcp": {
"type": "stdio",
"command": "web-fetch-mcp",
"env": {
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY"
}
}
}
}
```
Note: If you encounter network access issues (e.g., unable to connect to Gemini), you can configure the environment variables HTTPS_PROXY and HTTP_PROXY. By default, the gemini-2.5-flash model is used, consistent with Gemini-CLI.
If you are running from a local clone:
```json
{
"mcpServers": {
"web-fetch-mcp": {
"type": "stdio",
"command": "node",
"args": ["/path/to/web-fetch-mcp/dist/index.js"],
"env": {
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY"
}
}
}
}
```
## Tool Reference
The service provides the following tools:
- `summarize_web`: Summarizes content from one or more URLs.
- `compare_web`: Compares content across multiple URLs.
- `extract_web`: Extracts specific information from web content using natural language prompts.
### Example Tool Call
To use a tool, your agent should make a `callTool` request specifying the tool name and a `prompt`:
```json
{
"tool": "summarize_web",
"arguments": {
"prompt": "Summarize the main points from https://example.com/article"
}
}
```
## For Developers
If you wish to contribute to the development of this server:
1. **Clone the repository:**
```bash
git clone https://github.com/your-username/web-fetch-mcp.git
cd web-fetch-mcp
```
2. **Install dependencies:**
```bash
npm install
```
3. **Run in development mode:**
```bash
npm run dev
```
4. **Build for production:**
```bash
npm run build
```
TDQS
A3.5/5.0
Scored across 3 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: summarize gives an overview, compare contrasts multiple URLs, and extract pulls specific details. No ambiguity between them.
Naming Consistency5/5
All tool names follow the same verb_web pattern (summarize_web, compare_web, extract_web), making the set predictable and easy to navigate.
Tool Count5/5
Three tools is appropriately scoped for a web-content analysis server. Each tool addresses a distinct need without unnecessary bloat.
Completeness4/5
The toolset covers the core operations of summarization, comparison, and extraction. A minor gap is the absence of a raw fetch tool, but the server's purpose is focused on processed output, so this is acceptable.
Maintenance
ActivityInactive
ResponsivenessNo issues