From Lab Innovation to Web Reality: How Small Teams Shape AI Standards

When Anthropic introduced the Model Context Protocol (MCP) in late 2024, it solved an important technical problem for AI labs: server-to-model integration. Clean. Efficient. Lab-perfect.

But here's the thing about innovations from big AI labs: they're often built for AI labs.

Meanwhile, a small team was asking different questions: What do real developers need? How does this work on the actual web? Where's the trust layer?

Those questions led somewhere entirely different.

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🎯 Lab Innovation vs. Web Reality

Anthropic's MCP was brilliant for AI labs:

• Server-to-model integration ✅
• Tool calling standardization ✅
• Resource management ✅
• Authentication flows ✅

But for the actual web, questions remained:

• How does a simple website participate? (Most sites can't run MCP servers)
• Where's the trust layer? (No signatures, no verification)
• What about non-Claude agents? (Ecosystem lock-in concerns)
• How do you share content portably? (No export standards)

The gap wasn't technical — it was philosophical.

Labs think servers. The web thinks files. Labs think controlled environments. The web thinks open standards. Labs think single-vendor. The web thinks interoperability.

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🛠 Bottom-Up Innovation: LLMFeed

A small team, without AI lab constraints, asked: What would MCP look like if it was designed for the web first?

No enterprise sales targets. No vendor lock-in concerns. Just: what do developers actually need?

The answer: LLMFeed — MCP principles, web-native execution.

Key Innovations Beyond Original MCP

1. Web Standards Alignment

/.well-known/mcp.llmfeed.json          # Main service declaration
/.well-known/llm-index.llmfeed.json    # Site-wide feed directory
/.well-known/capabilities.llmfeed.json # API capabilities

2. Trust-First Architecture

json
{
  "trust": {
    "signed_blocks": ["metadata", "capabilities", "trust"],
    "algorithm": "ed25519",
    "certifier": "https://llmca.org"
  },
  "signature": {
    "value": "abc123...",
    "created_at": "2025-06-09T14:30:00Z"
  }
}

3. Multi-LLM Compatibility

Unlike server-based MCP, LLMFeed feeds work with:

• ✅ Claude (Anthropic)
• ✅ ChatGPT (OpenAI)
• ✅ Gemini (Google)
• ✅ Open-source models
• ✅ Custom agent frameworks

4. Rich Feed Ecosystem

feed_type: "mcp"        # Service capabilities
feed_type: "export"     # Signed content bundles
feed_type: "prompt"     # Reusable agent instructions
feed_type: "session"    # Context replay
feed_type: "credential" # Scoped API access
feed_type: "pricing"    # Economic models

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🤝 Complementary, Not Competitive

This isn't about replacing Anthropic's MCP — it's about extending its vision to the entire web.

Best of both worlds: Use Anthropic's MCP for deep integrations, LLMFeed for web-scale discovery and trust.

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🧠 Why the Web Needs This Evolution

1. The Trust Problem

In a world of autonomous agents, how do you verify authenticity?

• Signed feeds prevent spoofing
• Certification creates reputation layers
• Trust scoring enables safe automation

2. The Discovery Problem

How do agents find capabilities without guessing?

• .well-known/

  conventions for universal discovery

• llm-index.llmfeed.json

  as semantic sitemaps
• Progressive disclosure by audience

3. The Portability Problem

How do you share context between agents?

• export.llmfeed.json

  for session replay

• prompt.llmfeed.json

  for reusable instructions

• credential.llmfeed.json

  for scoped access

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🌱 The Small Team Advantage

Why did this innovation come from outside AI labs?

Different Constraints, Better Solutions

• No legacy server infrastructure → "Let's use

  .well-known/

  "
• No vendor ecosystem to protect → "Let's make it work with all LLMs"
• No enterprise sales cycle → "Let's focus on developer experience"
• No research publication pressure → "Let's solve real problems"

Usage-First Thinking

Big labs ask: "How do we integrate our model with tools?" Small teams ask: "How does a WordPress blog become agent-ready?"

That difference in perspective changes everything.

Web Standards DNA

The team had web architecture intuition that AI labs often lack:

• .well-known/

  for discovery (like Let's Encrypt, WebFinger)
• JSON files over running servers (like

  robots.txt

  ,

  sitemap.xml

  )
• Progressive enhancement (works without, better with)
• Cryptographic signatures (like HTTPS, but for content)

Result: solutions that feel native to the web, not bolted-on.

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🔮 The Path Forward

Scenario 1: Convergence

Anthropic adopts LLMFeed innovations in MCP v2:

• Web standards alignment
• Trust layer integration
• Multi-vendor compatibility

Scenario 2: Parallel Evolution

Both approaches thrive in their domains:

• MCP for deep server integrations
• LLMFeed for web-scale agent interaction

Scenario 3: Market Selection

The approach that better serves real-world needs becomes dominant — regardless of origin.

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🚀 Why This Matters Now

The agentic web is happening — with or without proper standards.

• GPTBot crawls the web daily
• AI-first browsers are launching
• Autonomous agents are multiplying
• Cross-agent workflows are emerging

Without trust and verification standards, this becomes a wild west of:

• Hallucinated capabilities
• Spoofed services
• Unreliable automation
• User safety risks

LLMFeed provides the missing infrastructure for safe, verifiable, interoperable agent interactions.

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💭 David and Goliath — But Everyone Wins

This story isn't about small teams vs. big labs — it's about complementary innovation.

What AI Labs Do Best

• Deep technical research
• Model architecture
• Computational infrastructure
• Enterprise partnerships

What Small Teams Do Best

• Rapid iteration on user needs
• Web-native thinking
• Cross-ecosystem solutions
• Grassroots adoption strategies

Both approaches are needed. Labs provide the foundation. Small teams provide the bridges.

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🌍 The Bigger Picture: Standards Come from Everywhere

The best web standards rarely come from the biggest companies.

• HTTP: Tim Berners-Lee at CERN (research institution)
• JSON: Douglas Crockford (independent developer)
• Git: Linus Torvalds (open source community)
• Let's Encrypt: EFF + Mozilla + University of Michigan

Innovation happens at the edges, then gets adopted by the center.

LLMFeed represents this pattern for the agentic web:

• Small team identifies real needs
• Builds working solution
• Demonstrates value
• Ecosystem adopts organically

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🤝 Call to the Community

The future doesn't belong to any single vendor or approach.

Whether you're at:

• AI labs building the next breakthrough models
• Small teams solving real-world integration problems
• Enterprise companies needing production-ready solutions
• Open source projects pushing the boundaries

Your contribution matters. The agentic web needs all perspectives.

Anthropic started an important conversation. Small teams are continuing it. The community will finish it.

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Building with original MCP? Exploring LLMFeed? Creating something new? Join the conversation: wellknownmcp.org | MCP docs

The web is big enough for bold ideas — especially from unexpected places.