[GitHub] daniel3303/Equibles
Equibles is a self-hosted, open-source financial data MCP (Model Context Protocol) server that serves as an alternative to Bloomberg Terminal, specifically designed for AI agents It scrapes, stores, and serves 13+ categories of financial data including SEC filings, XBRL financials, 13F holdings, insider/congressional trades, short data, FRED indicators, CFTC/CBOE positioning, and daily stock prices The self-hosted version provides 62 MCP tools with no account or API key required, running on Dock
Analysis
TL;DR
- Equibles is a self-hosted, open-source financial data MCP (Model Context Protocol) server that serves as an alternative to Bloomberg Terminal, specifically designed for AI agents
- It scrapes, stores, and serves 13+ categories of financial data including SEC filings, XBRL financials, 13F holdings, insider/congressional trades, short data, FRED indicators, CFTC/CBOE positioning, and daily stock prices
- The self-hosted version provides 62 MCP tools with no account or API key required, running on Docker on your own hardware
- Equibles Cloud extends the open-source core with additional premium features including earnings call transcripts/audio, real-time quotes, options chains with Greeks, LLM-extracted KPIs, IPO filings, and a full US-market screener
- It exposes all data over the Model Context Protocol, enabling direct querying by Claude, ChatGPT, Cursor, and other AI agents
Why It Matters
Equibles represents a significant shift toward democratizing institutional-grade financial data access for AI-powered applications, removing the traditional paywall barrier of proprietary terminals like Bloomberg. For AI practitioners, it provides a standardized MCP-based interface to rich financial datasets, enabling the development of sophisticated financial analysis agents without expensive subscriptions or complex data engineering pipelines.
Technical Details
- Architecture: Docker-based self-hosted deployment with a Model Context Protocol (MCP) server exposing 62 tools for AI agent integration
- Data Sources: Scrapes from SEC EDGAR (10-K, 10-Q, 8-K filings), SEC XBRL (parsed financial statements), SEC 13F-HR (institutional holdings), SEC Forms 3/4/5/144 (insider trading), FINRA/SEC short data, FRED economic indicators, Yahoo Finance (OHLCV + technical indicators), CFTC Commitments of Traders, CBOE VIX/put-call ratios, USAspending.gov (government contracts), and FDA.gov (AdComm calendar)
- Cloud Extensions: Earnings call transcripts and audio, real-time licensed market quotes, options chains with full Greeks (delta/gamma/theta/vega), LLM-extracted KPIs and guidance, buyback/ATM data, IPO filings, executive changes, valuation multiples, and a US-market screener
- Access Model: Free self-hosted option (62 tools, no account/key) and cloud tier with 100 free daily requests; per-client setup guides available
Industry Insight
- The MCP standardization approach positions Equibles as infrastructure for the emerging "AI agent economy" in finance, where multiple agent frameworks can share a common data protocol rather than building custom integrations
- The freemium model (self-hosted core + cloud premium) mirrors patterns seen in developer tools, suggesting a viable path to monetization while maintaining open-source adoption as a distribution strategy
- By targeting AI agents rather than human traders, Equibles opens a new market segment for financial data providers, potentially accelerating the integration of real-time financial reasoning into production AI applications
Disclaimer: The above content is generated by AI and is for reference only.