Keenable SELECT: an agent that searches the web in SQL

Keenable has launched Keenable SELECT, an MCP server that lets users query the live web with read-only DuckDB SQL, running one SELECT statement to search over 1,000 pages, filter with exact WHERE clauses at no LLM cost, and extract fields with a single small LLM call per row. The tool, demonstrated in a gallery of research reports, pairs a research agent that iteratively runs SQL queries with a report agent that generates shareable HTML pages from the result sets.

Research reports built by Keenable SELECT https://app.keenable.ai/select/start , an agent that searches the web in SQL. Every card links the finished report and the full trajectory behind it: each query, tool result, and result set. You ask “Which AI researchers moved between frontier labs since 2025? For each move list the researcher, the lab they left, where they went and the month.” Keenable SELECT runs SQL on the web SELECT SEM EXTRACT content, 'researcher' , SEM EXTRACT content, 'left lab' , SEM EXTRACT content, 'joined lab' , SEM EXTRACT content, 'move month' FROM WEB SEARCH 8 diverse queries WHERE SEM MATCH content, 'named researcher moving between frontier labs, 2025+' Keenable SELECT is an MCP server with one main tool: select . The tool runs one read-only DuckDB SELECT statement on live web data. The server runs the web and semantic operators outside DuckDB, puts their output back into the row set, and then runs the final SQL in DuckDB. A traditional web search gives an agent ten links. The agent must then read each page and build the answer from expensive tokens. SELECT moves this work into the query. One call can search more than 1,000 pages, filter them with an exact WHERE clause at no LLM cost, extract fields with one small LLM call per row, and group the rows. select takes DuckDB SELECT queries and returns the rows. The server saves every query result as a result set with an id, and a later query can read from that id. generate html report takes a brief and result set ids. A report model on the server writes an HTML report from the rows and returns a shareable link.The operators live inside normal SQL. The server finds them in the parsed statement, runs them, and replaces them with plain columns. Exact SQL filters run first, so only the surviving rows go to the LLM operators. | Operator | What it does | |---|---| WEB SEARCH 'q1', 'q2', ... | Searches all queries at the same time, merges ranked results, and removes repeated URLs. | WEB FETCH 'https://a.com', ... | Gets the given URLs as Markdown, one row per page. | SEM EXTRACT column, 'field description' | One LLM call per row. It returns one field, or null when the text does not give the value. | SEM EXTRACT ALL column, 'what one value is' | Like SEM EXTRACT , but returns all matching values in a list. | SEM MATCH column, 'predicate' | An LLM test per row. Use it as a meaning-based WHERE filter. | SEM SCORE column, 'query' | A low-cost embedding score per row. Use ORDER BY ... DESC LIMIT k . | SEM NORM column | Gives the same key to values with the same meaning. Use it in GROUP BY . | WEB SEARCH and WEB FETCH can also run per row. Their arguments can use row columns, for example WEB SEARCH name || ' founding year' . Every report in this gallery comes from two agents: a research agent that uses the MCP server to gather the data, and a report agent that runs inside generate html report on the server and writes the page. The research agent is a plain tool loop: an LLM with the select tool. It writes and runs its own queries until it can answer, and streams its tool calls, results, and answer as events. A follow-up question continues the conversation on top of the stored transcript. Every run in this showcase asks for an HTML report, so the agent ends each answer with the report link. A second agent writes each report on the server. It gets the brief, the rows of the result sets, and an authoring guide. It builds the page in a sandboxed Python session that holds the result sets as dataframes, so the data reaches the page without the model retyping it. After each publish, the server renders the draft and returns screenshots and the page's JavaScript error count; the agent fixes the document and publishes again, under a fixed budget. Only the final draft stays live, published as a link.