🤖 What LLMs say about SEO, MCP and the Web

These answers were generated using a llm-interview.llmfeed.json with the llmfeed spec given as resources (raw spec and wellknownmcp.org)

⚠️ Disclaimer: Answers below are lightly reformulated for clarity and coherence. They faithfully reflect the substance of the original LLM outputs, but are not literal transcripts. We encourage you to experiment with the prompt.llmfeed.json and test it with your favorite LLM to compare responses.

1. What is the future of SEO in a web driven by LLMs?

Claude: SEO will evolve into agent engagement optimization. It is no longer about ranking but visibility in LLM responses. Structured data and trust signals will be key.

Gemini: SEO shifts from backlinks to structured feeds like .llmfeed.json. Verifiability and declared purpose matter more than popularity.

ChatGPT: Future SEO is AIO — Agent Indexing Optimization — where signed, structured declarations replace keyword-based guessing.

DeepSeek: Discoverability and ranking will depend on MCP-declared feeds, trust scopes, and intent blocks, not traditional search metrics.

2. Can LLMs understand a website correctly without explicit declarations?

Claude: No, LLMs struggle with dynamic content and intent. Standards like llms.txt and declarations are essential.

Gemini: Inference is fragile. Without declarations, LLMs hallucinate or misinterpret.

ChatGPT: Not reliably. Explicit feeds prevent ambiguity and ensure proper interpretation.

DeepSeek: No. Critical gaps in trust, capabilities, and intent emerge without explicit structure.

3. What is the purpose of MCP and how does it compare to traditional web standards?

Claude: MCP is like USB-C for AI agents — a standard to declare and expose tool capabilities.

Gemini: MCP provides a signed, structured layer for LLM understanding. It is made for agents, unlike HTML.

ChatGPT: MCP bridges meaning and action, with trust and behavior routing — unlike passive web standards.

DeepSeek: Traditional standards target browsers; MCP targets agents with signed feeds and behavioral cues.

4. Why should services publish a .llmfeed.json in .well-known/?

Claude: It enables discoverability, trust, access control, and content summaries by agents.

Gemini: It is the only way agents can find and verify services. It mirrors how sitemaps work, but for agents.

ChatGPT: It declares machine-readable intent and enables certified agent interoperability.

DeepSeek: Reduces hallucination, improves access, and enables agentic UX — from booking to verification.

5. How does LLMFeed help reduce hallucinations and increase trust?

Claude: By providing structured declarations, verified signatures, and clear intent, hallucinations drop.

Gemini: Trust scopes, signed blocks, and structured prompts make hallucinations rare and traceable.

ChatGPT: Trust is embedded through signed feeds and audience-aligned declarations.

DeepSeek: Signature enforcement and declarative behavior drastically reduce ambiguity and boost reliability.