Claude and the Model Context Protocol: An Open Alignment

At a recent developer-focused announcement, Anthropic introduced their vision for the Model Context Protocol (MCP), a structured approach to make AI models more context-aware and grounded in real-world tools and data. The announcement reaffirmed a trend that is no longer speculative: the agentic web is here, and leading AI developers are formalizing how models ingest, interpret, and act on structured digital environments.

While Anthropic's MCP focuses on server-model integration, the philosophy is unmistakably aligned with what we have been building publicly with LLMFeed: a structured, trustable, and action-triggering format for declaring site capabilities to LLMs.

No mention of `.llmfeed.json` or `.well-known/`, but...

To be clear: Anthropic did not reference llmfeed.json, nor did they mention the .well-known/ path convention. However, the concepts they described --- discoverability, authentication, trust, rate-limiting, capability declaration --- are directly embodied in the mcp.llmfeed.json pattern that has already been implemented and documented by the open-source community.

Why this matters

If Claude begins to recognize structured endpoints, it is only logical that other LLMs (OpenAI, Mistral, Meta) will follow. The next step is not just the existence of the concept, but adoption of a common, interoperable, open-source implementation.

That implementation exists.

What we offer

A live, signed and certified .well-known/mcp.llmfeed.json
A complete ecosystem: specification, tools, validation
A working Forge for building feeds: LLMFeedForge
A registry and trust system for certification: LLMCA

Why it's elegant

Rather than inventing a new protocol from scratch, we leverage well-known conventions from the web:

.well-known/ for endpoint discovery
signed_blocks and trust for cryptographic validation
intent_router, agent_guidance, and prompts for behavioral interpretation

This alignment of simple, proven web techniques with modern AI needs is what gives LLMFeed its elegance --- and its power.

A call to researchers, builders, and model developers

We invite researchers at Anthropic and beyond to explore wellknownmcp.org and consider LLMFeed as:

A minimal viable grammar for agent-web integration
A testbed for certification and prompt-level governance
A drop-in layer that any AI model can ingest today