🔍 Agent Website Comparison
Generate the perfect prompt to test how AI agents understand any website
Quick Demo
Compare what AI agents can understand from any website with MCP feeds vs traditional metadata extraction.
💯 Soyons honnêtes
On a essayé de coder un comparateur automatique... et c'est pas ouf. Le meilleur test, c'est le vôtre.
Plutôt que de simuler ce qu'un agent ferait, on génère le prompt parfait pour que VOTRE LLM fasse la vraie comparaison avec SES yeux.
Generate Your Analysis Prompt
Implémentez votre .well-known et soyez une référence de notre ecosystem
Ready for Even Better Results?
For more insightful analysis, train your LLM first to become an MCP expert. It'll understand the nuances and provide much more valuable comparisons.
🏆 Real Test Results
We used this exact prompt to compare OpenAI.com vs WellKnownMCP.org. Here's what happened with a real, unbiased AI agent:
❌ OpenAI.com (Traditional)
Technical Issues:
- • Direct fetch failed: "UNKNOWN_ERROR"
- • Required 7+ web searches as fallback
- • Information scattered across domains
Performance:
- • ~15,000+ tokens consumed
- • 30+ seconds discovery time
- • 70% confidence in answers
- • 6/10 information quality
✅ WellKnownMCP.org (MCP)
Technical Success:
- • Perfect fetch: 2/2 endpoints accessed
- • Structured JSON data immediately
- • Zero technical barriers
Performance:
- • ~2,000 tokens consumed
- • 2 seconds discovery time
- • 95% confidence in answers
- • 9/10 information quality
| Metric | OpenAI.com | WellKnownMCP.org | Advantage |
|---|---|---|---|
| Discovery Cost | ~$0.015 | ~$0.002 | 7.5x cheaper |
| Time to Useful Info | 30+ seconds | 2 seconds | 15x faster |
| Tokens per Fact | ~400 | ~50 | 8x more efficient |
| Confidence Level | 70% | 95% | 25% more reliable |
"MCP represents a paradigm shift from 'search and hope' to 'fetch and know' - transforming the web from a human-readable document library into an agent-accessible knowledge graph. Even with current technical limitations, MCP provides 7-8x token savings and 95% confidence versus traditional web crawling."
Gemini's Weighting System:
- • 40% Economic Efficiency (token cost)
- • 30% Service Discovery (actionable capabilities)
- • 20% Trust & Reliability (confidence level)
- • 10% Information Quality (richness vs noise)
Final Weighted Scores:
Key Findings:
- • Efficiency: "100x more structured info for a fraction of token cost"
- • Effort: "Minimal effort" vs "Significantly higher effort"
- • Capability: "Clear, exploitable capabilities list" vs "Must infer and actively search"
- • Trust: "Maximum confidence with cryptographic signature" vs "Rely on domain name"
"This analysis demonstrates in a flagrant manner the superiority of a structured and semantic web for AI agents.The MCP protocol transforms a website from a document to read into a partner to interact with.With the MCP approach, I can go from 'describing what the site says' to 'using what the site offers to help you.'"
💡 This test used the exact prompt generator above.Your results will be similarly dramatic.
💡 Pro tip: Results are much more insightful after using our 🥋 LLM training system