Eugene Intelligence
Financial data for AI agents. SEC XBRL fundamentals, insider trades, 13F holdings, treasury yields. Source-traced.
Eugene Intelligence v0.8
Financial data infrastructure for AI agents. Every number traced to source.
The first MCP-native financial data platform. One tool call gets you normalized SEC financials, 50+ computed ratios, live prices, technicals, crypto, and macro data — all with full provenance tracking. Built with async HTTP, persistent disk caching, and comprehensive error handling.
Quick Start
pip install -e .
# Three ways to run
eugene caps # CLI — list all capabilities
python eugene_server.py # REST API + MCP on port 8000
python eugene_server.py --mode mcp # MCP stdio server (for Claude Desktop)
What You Can Do
# SEC fundamentals with clean IS/BS/CF grouping
eugene sec AAPL -e financials -l 5
# 50+ financial ratios (PE, ROE, margins, leverage, growth)
eugene sec AAPL -e metrics
# Technical indicators (SMA, EMA, RSI, MACD, Bollinger, ATR, VWAP)
eugene sec AAPL -e technicals
# Daily price bars
eugene ohlcv AAPL -i daily --from 2026-01-01 --to 2026-03-14
# Live crypto quotes
eugene crypto BTCUSD
# Corporate actions (dividends + splits + 8-K events merged)
eugene sec AAPL -e corporate_actions
# Download as CSV
eugene export AAPL -f csv
# Stock screening
eugene screener --sector Technology --market-cap-min 1000000000
17 Extract Types
| Extract | Description | Source |
|---|---|---|
profile | Company name, CIK, SIC, address | SEC Submissions |
filings | Filing list with accession + URL | SEC Submissions |
financials | Normalized IS/BS/CF with provenance | SEC XBRL CompanyFacts |
concepts | Raw XBRL concept time series | SEC CompanyFacts |
insiders | Form 4 insider trade filings | SEC EDGAR |
ownership | 13F institutional holdings | SEC EDGAR |
events | 8-K material events | SEC EDGAR |
sections | MD&A, risk factors, business text | Filing HTML |
exhibits | Exhibit list with URLs | Filing Index |
metrics | 50+ financial ratios (7 categories) | XBRL + FMP Market Data |
ohlcv | Daily OHLCV price bars | FMP Historical Charts |
technicals | SMA/EMA/RSI/MACD/Bollinger/ATR/VWAP | Computed from OHLCV |
segments | Business + geographic revenue segments | SEC XBRL Dimensions |
float | Float shares, outstanding, free float | FMP Shares Float |
corporate_actions | Dividends, splits, 8-K events merged | FMP + SEC EDGAR |
transcripts | Earnings call transcripts with Q&A | SEC EDGAR 8-K |
peers | Relative valuation vs sector peers | SEC XBRL + FMP |
28 Canonical Concepts
Financials are normalized into clean IS/BS/CF groupings:
Income Statement: revenue, net_income, operating_income, gross_profit, eps_basic, eps_diluted, cost_of_revenue, ebitda (derived)
Balance Sheet: total_assets, total_liabilities, stockholders_equity, cash, total_debt, current_assets, current_liabilities, inventory, accounts_receivable, accounts_payable, short_term_debt, long_term_debt
Cash Flow: operating_cf, capex, free_cf (derived), depreciation_amortization, dividends_paid
Other: shares_outstanding, interest_expense
REST API
GET / API discovery
GET /health Health check
GET /v1/capabilities All 19 extracts listed
GET /v1/sec/{identifier} SEC data (any extract)
GET /v1/sec/{ticker}/ohlcv OHLCV price bars
GET /v1/sec/{id}/export CSV flat file download
GET /v1/screener Stock screener
GET /v1/crypto/{symbol} Crypto quotes
GET /v1/stream/filings SSE real-time SEC filing alerts
GET /v1/economics/{category} FRED macro data
Examples
# Fundamentals (FY, 5 years)
curl "localhost:8000/v1/sec/AAPL?extract=financials&period=FY&limit=5"
# Financial ratios
curl "localhost:8000/v1/sec/AAPL?extract=metrics&limit=1"
# OHLCV bars
curl "localhost:8000/v1/sec/AAPL/ohlcv?from=2026-01-01&to=2026-03-14"
# Crypto
curl "localhost:8000/v1/crypto/BTCUSD"
# CSV export
curl "localhost:8000/v1/sec/AAPL/export?format=csv&limit=3"
# Real-time filing stream (SSE)
curl "localhost:8000/v1/stream/filings"
# Economics
curl "localhost:8000/v1/economics/inflation"
Response Shape
Every response includes full provenance:
{
"status": "success",
"identifier": "AAPL",
"resolved": {"ticker": "AAPL", "cik": "0000320193", "company": "Apple Inc."},
"data": {
"periods": [{
"period_end": "2025-09-27",
"metrics": { ... },
"income_statement": { ... },
"balance_sheet": { ... },
"cash_flow_statement": { ... }
}]
},
"provenance": [{"source": "SEC CompanyFacts (XBRL)", "url": "..."}],
"metadata": {"service": "eugene-intelligence", "version": "0.8.0"}
}
Every financial metric traces back to its XBRL source:
{
"revenue": {
"value": 416161000000,
"unit": "USD",
"source_tag": "us-gaap:RevenueFromContractWithCustomerExcludingAssessedTax"
}
}
MCP (Model Context Protocol)
Claude Desktop
{
"mcpServers": {
"eugene": {
"command": "python3",
"args": ["eugene_server.py", "--mode", "mcp"]
}
}
}
5 MCP Tools
| Tool | Description |
|---|---|
sec | All SEC EDGAR data — 19 extract types via one tool |
economics | FRED macro data (inflation, employment, GDP, housing, rates) |
screener | Stock screening by sector, market cap, price, volume, beta |
crypto | Live crypto quotes (BTC, ETH, SOL, etc.) |
caps | Lists all capabilities and parameters |
Streamable HTTP
The server also exposes MCP via streamable HTTP at /mcp and SSE at /sse, so any MCP client can connect over the network.
Architecture
eugene_server.py FastAPI + MCP entry point (REST + stdio + SSE + streamable HTTP)
eugene/
router.py Request parsing, routing, envelope (19 handlers)
resolver.py ticker/CIK/accession -> identity
concepts.py 28 canonical concept mappings (XBRL -> stable keys)
cache.py L1 in-memory + L2 persistent disk cache
rate_limit.py Sync + async rate limiters
errors.py Error taxonomy (NotFound, Source, Validation, RateLimit)
auth.py API key authentication
cli.py Click CLI (eugene command)
handlers/
financials.py Normalized IS/BS/CF with derived metrics
metrics.py 50+ financial ratios (7 categories)
technicals.py SMA/EMA/RSI/MACD/Bollinger/ATR/VWAP
ohlcv.py OHLCV daily price bars
segments.py XBRL dimension parsing (business/geographic)
float_data.py Share float data
corporate_actions.py Dividends + splits + 8-K events merged
export.py CSV flat file generation
filings.py, profile.py Filing list, company profile
concepts_raw.py Raw XBRL concept series
sections.py MD&A, risk factors text extraction
insiders.py, ownership.py Form 4, 13F filings
events.py, exhibits.py 8-K events, exhibit list
options.py, orderbook.py Coming soon stubs
sources/
sec_api.py All SEC HTTP calls (one place)
fmp.py Market data (prices, OHLCV, screener, crypto, float)
fred.py Economic data (FRED)
Environment Variables
SEC_USER_AGENT=Eugene Intelligence ([email protected])
SEC_CONTACT_NAME=Eugene Intelligence
[email protected]
FMP_API_KEY=your_fmp_key
FRED_API_KEY=your_fred_key
PORT=8000
Built for agents that need to get finance right.
相关服务器
Binance Cryptocurrency MCP
Access real-time Binance cryptocurrency market data, including prices, order books, and trading history.
GrantAi
Deterministic O(1) memory for AI agents — local-first, MCP-native, with multi-agent speaker attribution and millisecond recall.
Hydrolix
Hydrolix time-series datalake integration providing schema exploration and query capabilities to LLM-based workflows.
RDW MCP Server
Query Dutch RDW vehicle registration data for vehicle information, fuel, and emissions data using the official RDW open data API.
Microsoft SQL Server
A Model Context Protocol (MCP) server for connecting to and querying Microsoft SQL Server databases.
Simple PostgreSQL MCP Server
An MCP server for interacting with PostgreSQL databases using tools, resources, and prompts.
PostgreSQL MCP Server
A Kotlin-based MCP server for interacting with PostgreSQL databases.
MySQL MCP Server
An MCP server for accessing and managing MySQL databases.
LoL Data MCP Server
Provides real-time, structured access to League of Legends game data, including champions, items, abilities, game mechanics, and patch information.
Python MSSQL MCP Server
A Python MCP server for Microsoft SQL Server, enabling schema inspection and SQL query execution.