MCP is for tools. A2A is for agents. What's for websites?
HTTP lets agents fetch pages. Cloudflare's Markdown for Agents lets them fetch more efficiently. MCP (Anthropic) connects agents to developer-defined tools. A2A (Google) lets agents delegate to other agents. But there's a missing layer: how does an agent execute a multi-step task on a website -- add to cart, fill a form, complete a checkout - with the site owner's consent and visibility? Today's agents either scrape (no consent, no structure) or the site builds a separate API (expensive, doesn't cover the long tail). The web's original protocols assumed someone is looking at a screen. That…
What it does
In the maker’s words, at launch
HTTP lets agents fetch pages. Cloudflare's Markdown for Agents lets them fetch more efficiently. MCP (Anthropic) connects agents to developer-defined tools. A2A (Google) lets agents delegate to other agents. But there's a missing layer: how does an agent execute a multi-step task on a website -- add to cart, fill a form, complete a checkout - with the site owner's consent and visibility? Today's agents either scrape (no consent, no structure) or the site builds a separate API (expensive, doesn't cover the long tail). The web's original protocols assumed someone is looking at a screen. That assumption is breaking. We wrote a whitepaper mapping the full protocol landscape - Cloudflare's Pay Per Crawl and Web Bot Auth (RFC 9421), MCP, A2A, x402, llms.txt - and categorizing 5 distinct agent architectures (text-based, CUA/screenshot, DOM-based, API-calling, hybrid). Each needs different discovery, execution, and identity mechanisms. We think MCP, A2A, and execution protocols are complementary layers, not competitors. The paper draws parallels to TCP/HTTP design decisions. Rover is our attempt at the execution layer. It's a DOM-native SDK the site owner installs. The Agent Task Protocol is one HTTP endpoint: POST /v1/tasks with { url, prompt }. Agents get back a task URL supporting JSON polling, SSE, or NDJSON. The site controls what agents can do and gets analytics on what they actually did. We're probably wrong about some of this -- would appreciate the feedback. Paper: https://www.rtrvr.ai/blog/agent-web-protocol-stack Code: https://github.com/rtrvr-ai/rover (FSL-1.1-Apache-2.0)
Does the same job
all alternatives →
Is Your Site Agent-Ready? by CloudflareApr 2026 · ▲264Scan your website to see how ready it is for AI agents.
The new Firecrawl MCPAug 2026 · docs.firecrawl.dev · ▲108Agent-ready web context for any MCP client.
- PAProduct analytics (and evals) for agent sessions on your MCPAug 2026 · armature.tech · ▲42
Hi HN! We’re Theodore and Louis, founders of Armature (YC P26). We reconstruct the entire session behind the MCP tool calls you receive, including what the user asked their agent to do and what the agent thought. You wrap your MCP in 3 lines of code (our SDK is available in Typescript, Python and Go) and start seeing in your dashboard: - All sessions reconstructed: it’s like reading the real conversation the user had inside Claude or ChatGPT! - A ranking of your MCP most popular use cases, built from sessions clustering - The most frequent issues your users’ agents encounter so you can fix…
- UMUniversal MCP gateway for AI agentsOct 2025 · mcp360.ai · ▲6
Hey HN Community, I built MCP360 after spending weeks integrating APIs for an AI agent project. Each tool needed its own setup, auth, billing, and maintenance. When any APIs changed, my integrations broke. I got tired of it. MCP360 is a single gateway giving AI agents access to 100+ tools through one config block. Search engines, web scraping, SEO, e-commerce data, maps, domain tools, and more. Real example: Instead of managing Google Search, web scraping, SERP tracking, and keyword research as 4 separate subscriptions (4 bills, 4 auth systems, 4 points of failure), you connect once. Works…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, April 2026
the whole month →
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Thought the resources for GPU arch were lacking, so here we are
Life & fun · Apr 2026 · jaso1024.com
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Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.
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Life & fun · Apr 2026 · sam-burns.com
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With social media and now AI, its important to keep the indie web alive. There are many people who write frequently. Blogosphere tries to highlight them by fetching the recent posts from personal blogs across many categories. There are two versions: Minimal (HN-inspired, fast, static): https://text.blogosphere.app/ Non-minimal: https://blogosphere.app/ If you don't find your blog (or your favorite ones), please add them. I will review and approve it.
AI · Apr 2026 · text.blogosphere.app