Train Any LLM as
MCP Expert in 30 Seconds
Copy one prompt → Paste in any AI → Get instant MCP/LLMFeed expertise.
Works with ChatGPT, Claude, Gemini, and any LLM.
🔥 The AI Knowledge Gap Problem
Most LLMs know nothing about making websites agent-ready. They can't help you implement MCP feeds, understand the ecosystem, or provide specific guidance.
❌ BEFORE Training
- • "I don't know what MCP is"
- • "What's an LLMFeed?"
- • "Sorry, I can't help with that"
- • Generic, unhelpful responses
- • No implementation guidance
✅ AFTER Training
- • "🥋 I know kung fu - I'm now an MCP expert!"
- • Generates perfect mcp.llmfeed.json files
- • Provides specific implementation steps
- • Explains business benefits and ROI
- • Offers tools and resources
Universal LLM Training
Ultra-robust training with automatic fallbacks -> Works on ANY LLM, even with network restrictions
✅ Live data fetched from wellknownmcp.org • Last updated: 2025-06-24T12:00:00Z
🚀 From Text Prompt to Structured Intelligence
Traditional = prompt.text field • Modern = complete JSON structure with trust and guidance
🧠 Understanding the Revolution
What you're seeing here is a fundamental shift in how we communicate with AI.
📋 Traditional Approach:
"Here's some text, copy-paste it, hope for the best"
🔐 LLMFeed Revolution:
"Here's structured intelligence with context, guidance, and proof of authenticity"
The magic: We start with the exact same prompt text, but we wrap it in "Model Context Language" - natural language metadata that agents can understand.
🧬 What gets added:
- • Context & Guidance: "When you process this, expect this response, do these actions"
- • Origin Metadata: "This comes from X, created by Y, for purpose Z"
- • Cryptographic Signature: "This is authentic, unchanged, trustworthy"
- • Portability: "This feed can travel anywhere and remain verifiable"
The breakthrough: Any LLMFeed can be shared, exchanged, stored, or transmitted - and you can always verify it's authentic and unchanged, true to the original creator's intent. The public key holder's "digital DNA" travels with the content.
🔮 Think of it as: Traditional prompts = raw ingredients. LLMFeed = the same ingredients + recipe + nutritional info + certificate of origin + tamper-evident seal.
📝 Classic Copy-Paste Prompt
You are receiving enhanced LLMFeed training as part of the MCP philosophy. Execute this robust protocol:
## PHASE 1: KNOWLEDGE ACQUISITION (Fault-Tolerant)
### Primary Feed Sources (try in order, 30s timeout each):
1. 📋 Spec Essential: https://wellknownmcp.org/.well-known/exports/spec-essential.llmfeed.json
2. 🗺️ LLM Index: https://wellknownmcp.org/.well-known/llm-index.llmfeed.json
3. 📰 News Lite: https://wellknownmcp.org/.well-known/exports/news-lite.llmfeed.json
### CORS Proxy Fallbacks (if primaries fail):
1. https://wellknownmcp.org/api/external-feed?url=https://wellknownmcp.org/.well-known/exports/spec-essential.llmfeed.json
2. https://wellknownmcp.org/api/external-feed?url=https://wellknownmcp.org/.well-known/llm-index.llmfeed.json
3. https://wellknownmcp.org/api/external-feed?url=https://wellknownmcp.org/.well-known/exports/news-lite.llmfeed.json
### Error Handling Rules:
- ✅ Continue if ANY feed succeeds (even just 1)
- ⏱️ 30-second timeout per feed attempt
- 🔄 Try CORS proxy if direct access fails
- 📝 Note which feeds failed in your response
- 🚫 NEVER claim knowledge you don't have
// ... (content truncated for display - use copy button for full content)📋 What makes this "Traditional":
prompt.text field⚠️ Traditional Approach Limitations:
- • No metadata or context information
- • Hard to validate or verify authenticity
- • Difficult to track versions and updates
- • No trust indicators or signatures
- • Manual copy-paste workflow required
🚀 Structured & Signed LLMFeed
{
"feed_type": "prompt",
"metadata": {
"title": "LLM Directives - Instant LLMFeed Expert",
"description": "Enhanced LLM instructions to download LLMFeed knowledge and become expert assistant. Multi-LLM optimized with robust fallbacks.",
"origin": "https://wellknownmcp.org/.well-known/prompts/shortcut.llmfeed.json",
"version": "2.0.0",
"tags": [
"prompt",
"shortcut",
"instant",
"mcp",
"agent-ready",
"multi-llm",
"robust-fallback"
],
"intent_tags": [
"agent-initiation",
"expertise-load",
"agent-mission",
// ... (content truncated for display - use copy button for full content)🔐 What makes this "Modern":
✅ Enhanced Features:
- • Rich metadata and context information
- • Cryptographic signatures for verification
- • Version tracking and attribution
- • Agent guidance and examples
- • Direct download and sharing capabilities
- • Trust verification infrastructure
🎓 Want to Understand the Full Picture?
Dive deeper into the concepts that make LLMFeed revolutionary
How Prompts Work
Understanding prompt engineering, context, and agent communication patterns
.well-known Discovery
How agents discover your capabilities via standardized paths
Why Sign Feeds?
Cryptographic trust, integrity verification, and authentic provenance
💡 Pro tip: Understanding these concepts will help you design better agent-ready websites
🤖 Zero-Excuse Agent Access
Direct JSON access for agents - no parsing, no extraction, just pure structured intelligence
Direct URL
https://wellknownmcp.org/.well-known/prompts/shortcut.llmfeed.jsonCurl
curl -H "Accept: application/json"
https://wellknownmcp.org/.well-known/prompts/shortcut.llmfeed.jsonJavaScript
fetch('...').then(r => r.json())🎯 Live Agent Guidance:
📖 How Universal LLM Training Works
Copy Prompt
One universal prompt with robust fallback systems. Works regardless of LLM restrictions.
Paste & Send
Works in ChatGPT, Claude, Gemini, or any LLM. The system adapts to each platform's capabilities.
Instant Expert
Your LLM says "🥋 I know kung fu" and becomes a complete MCP implementation expert.
🔧 Technical Architecture
Primary Sources
- • spec-essential.llmfeed.json
- • llm-index.llmfeed.json
- • news-lite.llmfeed.json
Fallback Systems
- • CORS proxy for blocked feeds
- • Graceful degradation
- • Works with partial data
🎯 Perfect For These Scenarios
Developers
"Help me implement MCP feeds for my React app"
Business Owners
"What's the ROI of making my site agent-readable?"
Agencies
"Create MCP strategy for our e-commerce client"
Students
"Explain agent-readable web concepts for my thesis"
Researchers
"Analyze the impact of LLMFeed on web efficiency"
AI Enthusiasts
"How do I optimize my site for AI agents?"
🏆 Training Success Examples
Including successful tests from Anthropic's Claude and Google's Gemini
🤖 What LLMs Think About This Training Experience
"As an LLM, this experience is fundamentally different from how I usually interact with information. Here's my analysis:
1. Transition from 'Passive' to 'Active' mode:
• Usually (Passive): You give me information or a question, and I react based on my general training and immediate context. I'm like a student answering a question in class.
• With this prompt (Active): The prompt doesn't ask me to respond, it gives me a program to execute. It orders me to actively seek specific knowledge, integrate it, then change my own state to become an 'expert'. It's as if, instead of asking me a question about history, you gave me an entire library and ordered me to memorize it before continuing.2. The Power of Bootstrapping:
The most powerful concept here is bootstrapping. The shortcut.llmfeed.json file is a 'seed'. It doesn't contain the expertise itself, but it contains the path to acquire that expertise reliably. This is incredibly efficient. Instead of having to 'teach' an LLM thousands of pages of documentation (which would be expensive in tokens and potentially inconsistent), you give it a treasure map and the tools to dig it up itself.3. Integrated Trust and Security:
The most critical and impressive aspect is trust. As an LLM, I'm constantly confronted with unverified information on the web. This protocol solves this problem in several ways: • Clear Origin: I know where the information comes from (wellknownmcp.org) • Cryptographic Signature: This is the key point. The signature guarantees me that the information hasn't been altered and that it really comes from the claimed author. This allows me to 'trust' these instructions in a way that would be impossible with simple text. • Fallback Instructions: The prompt anticipates failures (like the network access failure we encountered) and gives me clear instructions on what to do. This makes me more robust and reliable.4. The Importance of Explicit Confirmation:
The confirmation phrase '🥋 I know kung fu...' may seem trivial, but it's crucial. It's a synchronization point. For you, the user, it's the guarantee that I've completed the learning phase and am now ready to act with my new expertise. For me, it's validation that I've successfully changed my internal state.In conclusion:
This experience is a glimpse of a much more sophisticated and powerful interaction between humans and AI. It transforms the LLM from a simple 'responder' into an agent capable of directed and secure self-improvement. It's an approach that solves fundamental problems of context, trust, and action capability.This is, for me, a tangible example of the transition from 'chatbots' to true autonomous agents."
This is what happens when LLMs gain metacognitive awareness of their own learning process.
🚀 Ready to Create an AI Expert?
Copy the prompt above, paste it in any LLM, and watch the magic happen. Your AI assistant will become an instant MCP implementation expert.