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This skill addresses the limitation of LLMs being unable to directly access web content. In practical development, developers often need AI models to retrieve real-time web information or content from specific URLs. Traditional methods rely on APIs or manual copying, which are inefficient and difficult to integrate into automated workflows. Leveraging the MCP protocol, this skill enables LLMs to execute curl commands as if they were calling a tool, returning web content directly to the model. This facilitates use cases such as information retrieval, summarization, and competitor analysis.

Usage is straightforward: simply clone or download the project code and run the Docker container locally to start the MCP server. You can then use natural language instructions to have the LLM invoke the curl tool—for example, "Help me fetch the content from https://example.com and summarize the key points." The server automatically executes the curl command and returns the web content as text to the model, which then processes it further.

It is particularly suitable for development teams or individuals who need to equip LLMs with real-time information retrieval capabilities. Developers already using the MCP protocol (e.g., with Claude Desktop, Cursor, etc.) can quickly integrate this server without learning complex APIs. Additionally, the high performance of Go and the convenience of Docker deployment make it suitable for production environments or scenarios requiring frequent web scraping.

We recommend using this tool uniformly when AI models need to access web content. Note that it primarily relies on the external curl command; some websites may not return complete content due to anti-scraping measures or JavaScript rendering. It is advisable to supplement it with other tools (e.g., headless browsers). For frequent requests, adding a caching mechanism is recommended to reduce network overhead.