A Model Context Protocol (MCP) server implementation that integrates with Dumpling AI for data scraping, content processing, knowledge management, AI agents, and code execution capabilities.
mcp-server-dumplingai is an MCP project maintained in the dumpling-ai/mcp-server-dumplingai repository. The maintainer describes its purpose as “A Model Context Protocol (MCP) server implementation that integrates with Dumpling AI for data scraping, content processing, knowledge management, AI agents, and code execution capabilities”. Read the project documentation for the complete scope.
The documented npm package is mcp-server-dumplingai. The configuration below comes from the maintainer’s example; adapt its paths and account values to your environment.
The example declares DUMPLING_API_KEY. Secret-looking values have been replaced with placeholders. Required and optional labels are shown only when the documentation states them.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
{
"mcpServers": {
"dumplingai": {
"command": "npx",
"args": [
"-y",
"mcp-server-dumplingai"
],
"env": {
"DUMPLING_API_KEY": "YOUR_DUMPLING_API_KEY"
}
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
| Variable | Description | Required |
|---|---|---|
| DUMPLING_API_KEY | Declared in the maintainer’s client configuration. | Yes |
Retrieve public web pages as readable Markdown with the reference Fetch MCP server, including pagination for longer responses.
Use Firecrawl MCP to retrieve website content and extract information for research through Firecrawl’s API-backed web tools.
The original Chromium automation reference server — simple, screenshot-driven browser control.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
Automates browsers and APIs, enabling Large Language Models (LLMs) to interact with web pages for tasks like screenshot capture and JavaScript
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.