AI Pulse by Inblix

Hugging Face's Tiny Agent: MCP strips agents down to a 50-line while loop

Hugging Face Blog · Apr 25, 2025 · 2 min read · Read original article →

Curated by the Inblix editorial team


Featured image for article: Hugging Face's Tiny Agent: MCP strips agents down to a 50-line while loop

The hype around the Model Context Protocol (MCP) has been deafening, but after weeks of digging into it, the reality is refreshingly simple. Hugging Face engineer Julien Chaumond strips away the mystique in a new walkthrough, showing that an MCP-powered agent is essentially just a while loop bolted onto an inference client.

Chaumond’s demo, which you can run right now with a single npx @huggingface/mcp-client command, connects a local agent to two MCP servers: one for your filesystem and one that controls a sandboxed Chromium browser via Playwright. When prompted to “write a haiku about the Hugging Face community and write it to a file named hf.txt on my Desktop,” the agent dutifully executes both steps. A second prompt asking it to search for HF inference providers and open the first three results shows the browser automation in action.

The secret sauce isn’t MCP itself—which Chaumond describes as merely “a standard API to expose sets of Tools”—but the fact that modern LLMs like Qwen2.5-72B-Instruct have native function-calling capabilities baked in. Tools are defined by a name, description, and a JSONSchema, a format largely dictated by OpenAI’s function-calling documentation. The inference engine passes these tool definitions to the model, which can choose to call zero, one, or several of them during generation.

This architecture means the heavy lifting happens entirely on the model side. The MCP client implementation, which lives in the huggingface.js monorepo, uses modern JavaScript async generators to handle streaming LLM responses elegantly. Chaumond points out that you can replace the Anthropic SDK in any MCP tutorial with any OpenAI-compatible client and get the same result. The default setup runs Qwen2.5-72B-Instruct on Nebius, but everything is configurable via environment variables. It’s a stark reminder that the infrastructure around agents is maturing faster than many realize—what looked like complex orchestration a year ago is now a fifty-line script.

💡 Key Takeaways

  1. An MCP agent requires only a while loop on top of an inference client—the model's native tool-calling handles everything else.
  2. The demo agent connects to local MCP servers for filesystem access and browser automation, executing multi-step tasks like web searches and file creation autonomously.
  3. MCP servers currently run as local processes, though remote server support is on the roadmap, which will change how developers deploy agent infrastructure.

Keep reading: See related articles below for more coverage on this topic.

Get smarter about AI

The sharpest AI news, curated daily. Delivered free to your inbox.

Learn more

Glossary terms

← Back to all articles