What transport method does mult-fetch-mcp-server use?
It implements the Standard Input/Output (Stdio) transport method as defined by the MCP specification.
Mult Fetch MCP Server
@lmcc-dev
Basic information
About Mult Fetch MCP Server
A versatile MCP-compliant web content fetching tool that supports multiple modes (browser/node), formats (HTML/JSON/Markdown/Text), and intelligent proxy detection, with bilingual interface (English/Chinese).
Mult Fetch MCP Server config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mult-fetch-mcp-server": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"@lmcc-dev/mult-fetch-mcp-server",
"--client",
"claude"
]
}
}
}Mult Fetch MCP Server tools
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Fetch the live tool list by running this server in a temporary sandbox using the button above.
Overview
What is mult-fetch-mcp-server?
mult-fetch-mcp-server is an MCP (Model Context Protocol)-compliant server that enables AI assistants to fetch and process web content. It supports multiple scraping methods (HTML, JSON, Markdown, plain text), intelligent content extraction, automatic chunking of large content, and bilingual English/Chinese interface.
How to use mult-fetch-mcp-server?
Install globally via pnpm add -g @lmcc-dev/mult-fetch-mcp-server or run directly with npx @lmcc-dev/mult-fetch-mcp-server. Integrate with Claude Desktop by adding a server configuration in claude_desktop_config.json using the npx method. Then restart Claude and use tools like fetch_html, fetch_json, fetch_txt, fetch_markdown, or fetch_plaintext.
Key features of mult-fetch-mcp-server
- Multiple web scraping methods: HTML, JSON, text, Markdown, plain text
- Intelligent content extraction using Mozilla's Readability library
- Automatic content size management and chunked retrieval
- Bilingual internationalization (English and Chinese)
- Support for Standard Input/Output (Stdio) transport
- Browser automation enhancements: scrolling, cookie management, selector waiting
Use cases of mult-fetch-mcp-server
- AI assistants fetching web page text for summarization or analysis
- Retrieving JSON data from APIs within conversational workflows
- Extracting clean Markdown from articles for further processing
- Accessing large content that exceeds context limits via chunked retrieval
Frequently asked questions
What are the runtime dependencies?
It requires Node.js (version indicated by Node.js badge) and is available as an npm package. For browser-based fetching, it manages a browser instance internally.
How can I set the language?
Set the `MCP_LANG` environment variable to `en` (English) or `zh` (Chinese). Default falls back to OS language then English.
How does content chunking work?
Large content is automatically split into manageable chunks. Clients can request specific chunks using parameters like `startCursor` and `contentSizeLimit`, and the demo client supports `--all-chunks` for sequential retrieval.
How do I enable debug logs?
Pass `"debug": true` in the tool call parameters. Debug messages are written to stderr and also to a log file when debug mode is active.
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