Design Computer

𝗪𝗵𝘆 𝗜 𝘄𝗿𝗼𝘁𝗲 𝗮 𝟳𝟮-𝗹𝗶𝗻𝗲 𝗠𝗖𝗣 𝘀𝗲𝗿𝘃𝗲𝗿 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝘁𝗿𝘂𝘀𝘁𝗶𝗻𝗴 𝟭𝟲𝟳 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀

I recently commissioned a workstation with an RTX 6000 Pro Blackwell for a client running local LLMs—specifically Qwen3.6-27B-MTP, Qwen3.6-35B-MoE, and nomic-embed-text—all served simultaneously via llama.cpp's router mode with multi-slot inference.

While I considered OpenWebUI or Page Assist for the client layer, llama.cpp now has its own WebUI with built-in MCP server support and a CORS proxy. It was the logical choice.

To keep the setup private, I'm running SearXNG on the same box. Local models are great for privacy, but routing searches through Google or Bing usually undoes that advantage; SearXNG keeps the search pipeline local. The missing piece was a bridge: an MCP server that exposes SearXNG's JSON API as a search tool over streamable-HTTP.

Here is the problem: the existing SearXNG MCP server on PyPI pulls in 167 transitive dependencies—including litellm, llama-index-core, and confluent-kafka. For what is ultimately a thin wrapper around a single HTTP endpoint, that is an unacceptable amount of supply chain surface area. Furthermore, most published options are stdio-first (aimed at Claude Desktop or IDEs), but the llama.cpp WebUI is browser-based and requires network transports.

So I wrote my own. One file. Fully auditable in a single sitting—72 lines of Python, powered by fastmcp and httpx. It works with llama.cpp WebUI, Page Assist, and any MCP client that speaks streamable-HTTP.

Note: This is built for local networks or VPNs, not public internet exposure. Bind to 127.0.0.1 or firewall it down if you're unsure.

Repo here: https://github.com/designcomputer/searxng-mcp-bridge

If you're running local models and care about the footprint of your toolchain (and where your search queries go), give it a look.

GitHub - designcomputer/searxng-mcp-bridge: Minimal SearXNG->MCP streamable-HTTP bridge for the llama.cpp WebUI Minimal SearXNG->MCP streamable-HTTP bridge for the llama.cpp WebUI - designcomputer/searxng-mcp-bridge

𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐯𝐬. 𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧: 𝐓𝐡𝐞 𝐅𝐥𝐚𝐰𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐫𝐨𝐩𝐨𝐬𝐞𝐝 𝐆𝐔𝐀𝐑𝐃 𝐀𝐜𝐭

AI safety is a critical conversation, but we must be careful not to solve one problem by creating several others.

I am currently opposing the proposed GUARD Act because it takes a blunt, overbroad approach to AI regulation that sacrifices privacy and access for all users. While the intention may be to protect young people, the actual impact would be concerning:

𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐑𝐢𝐬𝐤𝐬: Requiring users to upload government IDs or sensitive data just to access AI tools creates massive security vulnerabilities and undermines online anonymity.

𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐁𝐚𝐫𝐫𝐢𝐞𝐫𝐬: Blocking everyone under 18 removes essential tools that students already rely on for homework help, learning, and productivity.

𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐂𝐡𝐢𝐥𝐥𝐬: Vague rules paired with steep penalties will lead companies to "over-block," limiting features and access even where the law does not clearly require it.

We need AI regulations that address direct harms and promote transparency, rather than mandates that force users to trade their privacy for basic digital access.