
Forget Rigid Stock Screeners – A Universal Query API for Financial Data
Connect Your AI to Institutional-Grade Market Intelligence Plug any AI client, from ChatGPT to custom agents, directly into our financial data engine. Get real-time stock prices, fundamentals, institutional trading insights, and other financial data delivered through a universal Model Context Protocol (MCP) server.
Forget Rigid Stock Screeners – A Universal Query API for Financial Data launched on September 7, 2026 with 10 votes, #994 of 8,095 launches that month and more than 90% of that year's launches.
In plain words
A financial data API that connects AI applications to market intelligence through a universal query interface. Users can access real-time stock prices, company fundamentals, and institutional trading insights across 15,000+ stocks, 20,000+ funds, and other securities with over 20 years of historical data. The platform integrates with AI clients like ChatGPT and custom agents via Model Context Protocol, allowing flexible querying with multi-operator filters and customizable field selection from a single endpoint.
written from the facts on this page · October 2026
From the sources
Financial Data API provides market data, company fundamentals, alternative data, and much more. Over 20 years of historical data available, including information on 15.000+ stocks, 20.000+ funds, 2000+ ETFs, 13.000+ OTC securities.
Easily query financial datasets, apply multi-operator filters, and extract custom fields. Explore the Universal Query API documentation, endpoint parameters, and dataset specifications.
Instead of maintaining separate endpoints for each data type, use /api/v1/universal-query to access all our financial data from one place. Query parameters give you direct control over the response payload. You can select the dataset, request specific fields, and apply multiple filters – keeping payload sizes small and your integration clean. To maintain optimal performance, the maximum number of records returned per request is calculated dynamically based on query complexity, target dataset, and requested fields. Client applications must inspect the X-Record-Limit HTTP response header for the applied limit and use it with the offset parameter to paginate through the remainingfrom financialdata.net
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