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ReadyAI has launched its llms.txt MCP server, introducing a new infrastructure layer designed to make the web directly consumable for AI agents. The system has already processed more than 10,000 websites, converting them into structured, machine-readable datasets through its integration with Bittensor Subnet 33.
The release marks an early step toward what ReadyAI describes as a “semantic layer of the web,” where agents can query standardized data instead of relying on traditional scraping and parsing workflows.
A Shift Away From Scraping
At the core of the launch is a simple premise: today’s AI agents are inefficient consumers of web data.
Each time an agent needs information about a company, product, or domain, it typically scrapes raw HTML, parses unstructured content, and attempts to extract meaning. This process is repeated across countless agents, leading to duplicated effort, high compute costs, and inconsistent outputs.
In a post amplifying the launch, ReadyAI founder David Fields noted how the current system is fundamentally broken:
“Right now, every AI agent that needs info about a company or domain scrapes, parses, and hopes. Billions of redundant crawls. Trillions of wasted tokens. We’re building the infrastructure layer that fixes this — an indexed, machine-readable web powered by decentralized compute.”
ReadyAI’s llms.txt system replaces that process with deterministic lookup. Each supported domain is represented by a structured file containing semantic summaries, named entities, and topic classifications, allowing agents to retrieve information instantly without additional processing.
The dataset is accessible through ReadyAI’s MCP server and interactive search interface, where users can query domains and view structured outputs in real time. It's produced by Bittensor’s Subnet 33, where miners are tasked with crawling, cleaning, and structuring web data.
According to ReadyAI, each data point is validated through a scoring mechanism designed to prevent manipulation while enabling large-scale expansion.
The model positions structured data generation as a form of “useful proof of work,” aligning economic incentives with the creation of high-quality datasets.
Toward a Machine-Readable Web and Agentic Data Markets
The llms.txt launch is one component of a broader ReadyAI strategy centered on agent-focused data infrastructure.
The team is building toward a fully indexed, machine-readable web, where llms.txt files act as standardized “passports” for domains. Instead of crawling websites, agents would query these files directly, reducing redundancy and improving reliability across AI systems.
The company plans to scale coverage from 10,000 domains today to 100,000 in the near term, with a target of 1 million domains by the end of the year. Continuous updates sourced from datasets like Common Crawl are expected to expand and maintain coverage.
Beyond domain intelligence, ReadyAI is developing additional datasets, including “coding intelligence” derived from technical conversations and developer discussions. The approach is informed by research showing that reasoning in natural language before generating code can improve model performance.
Another area of focus is non-deterministic data enrichment, where multiple analytical perspectives are applied to the same source material to generate a wide range of valid outputs. This allows datasets to scale significantly without requiring entirely new inputs.
Ultimately, the company is working toward agentic commerce, where AI systems can autonomously discover, query, and pay for structured data. Payment rails tied to Subnet 33 tokens and USDC are expected to support this marketplace.
The current release includes the llms.txt MCP server, a dataset of over 10,000 structured domains, and an interactive search platform available at ReadyAI’s website, along with a Transcript Intelligence dataset that has already seen strong adoption.
View the repo - https://github.com/afterpartyai/llms_txt_store
Try the search - https://readyai.ai/
