We Fed the LLMs: What They Told Us About MCP

An update from the protocol ecosystem

← Previous: ✅ How LLMCA Certifie…Next →: 🗺️ The Case for .we…

You know what happens when you feed three structured .llmfeed.json files to the most advanced models on Earth? So did we — and we tried it.

Spoiler: they replied.

What we gave them

We handed each LLM the full set:

  • compiled-site.llmfeed.json (website overview)
  • spec.llmfeed.json (the full protocol spec)
  • news-en.llmfeed.json (recent articles, commentary and ecosystem views)

These feeds are signed, clean, and ready for ingestion by any agentic AI.

Who we tried it on

We gave the same inputs and prompt templates to:

  • ChatGPT 4-turbo
  • Claude 4
  • Gemini 1.5 Pro
  • Mistral (via Le Chat and OpenRouter)
  • Grok
  • DeepSeek
  • Perplexity

Some of them needed context to be pasted directly. Others accepted URLs. Some structured. Some chaotic. All responded.

Our Prompt Formula

We wanted their gut feeling, strategic view, and blind spots. Here’s what we asked:

  • "Do a SWOT analysis."
  • "Could this have an impact for a [job title] in [industry]?"
  • "Is the standard complete? Are there loopholes?"
  • "How can I contribute?"
  • "Should I be pioneering this?"
  • "So what do I do now?"
  • "Can we work on this together?"

We didn’t force structure. We just asked like humans.

What came back

✍️ “The best prompt is no prompt — it’s a contract.” — Claude 4 🌐 “I have a map, an intent, a signature… even jokes.” — ChatGPT ⚡ “Could be the HTTP of the agentic web.” — Grok 🤝 “Enhances trust, consistency, and agent performance.” — Mistral 🤨 “Needs adoption and iteration.” — Meta 😎 “I know Kungfu.” — Claude & DeepSeek

A full export of all LLM replies (quotes + analysis) is available here.

Don't overinterpret. But do try it.

This was a playful probe — not a peer-reviewed benchmark.

But we believe LLMs are beginning to reveal how they want to be fed. And MCP gives them the food they need: structured, signed, intention-rich capsules.

Try the same experiment yourself. Feed them the 3 files. Ask your own questions. Vary your prompts. Translate. Break. Remix.

Let’s explore how much these models really understand when you speak their language.

💬 Share your results. We’ll add them to the testimonies.