Scry

InstallQuestionsThe wireVectorsPricing The internet is vast. Your curiosities are deep. Programmatic internet research. Scry is an MCP that lets your agent run programs over billions of internet documents, for fun and profit. Reddit, Hacker News, LessWrong, arXiv, Stack Exchange, Wikipedia, and the prediction markets, with millions of new posts and comments landing every day. Take rows by the thousand, or the answer itself, computed over every queryable document that matches. Queryable now ≈158,708,972,872 rowsacross 43 sources · measured 16:20 UTC; counted every five minutes Held estate 413,247,662,227 rowsmeasured 16:20 UTC; counted every five minutes Landed, last 24 h +37,758,335,239 rows26.2M/min over the day to 16:20 UTC — rows newly held, live capture and backfill alike Three fast query trails turn along the angled city grid and jump across present and historical layers, leaving long-lived paths behind them. Thesis Run arbitrary programs over the internet. People search for pages; agents can query the underlying records. The question an agent actually has is rarely ten links. It might need every page that mentions this compound but not that patent, grouped by domain, since March. Scry runs that as a query instead of assembling it from repeated searches and scraped results. Each statement is read-only and runs under a deadline, a memory ceiling and a row cap on what returns; what a relation can answer quickly is on its schema entry. Questions How it works, and when your agent reaches for it. What do I install? Only the MCP server: https://mcp.scry.io, in ChatGPT, Claude, Claude Code, Codex, Cursor, or any MCP client. The sign-in creates your account. Nothing runs on your machine. Is there outside evidence? On Google's DeepSearchQA, 900 research questions graded exact-set, an agent doing its research through Scry answers 71.8% fully correct on a corpus that is still under half of Common Crawl; Google's Kaggle leaderboard reports its Gemini Deep Research Agent at 66.1%. Same judge model, same grading prompt, and every question, grade and tool-call transcript is on the DeepSearchQA page. How it works Your agent reads the live schema (/v1/scry/schema), picks an enabled source-native relation and listed vector helpers, then writes one bounded query for phrase matching, time windows, joins, or vector composition. Result rows keep the source-native identifiers and provenance fields the relation provides. What people do with it Agents compute over billions of documents instead of fetching one page at a time. A standing query watches the record for any condition you can write down; a derived table over the public web is one query and no crawler. Source requests and feedback go to POST https://api.scry.io/v1/feedback, no credential required. How fresh is the data? Per relation, and stated rather than promised: /v1/scry/schema carries each relation's observed extent and lag. Hacker News and LessWrong land in under 15 minutes. Papers move in layers — the merged academic.catalog advances when its recorded fold re-runs, and full text in academic.papers lands from its own extraction lane; the papers page states each. Mission Scry is public-benefit infrastructure, subsidized for independent noncommercial research and carried by commercial engagements: search over public evidence, every result traceable to its source.