deeplook
Researches any company in ~10 seconds using 10 data sources. Returns structured reports with bull/bear verdict for stocks, crypto, and private companies.
README
🔍 DeepLook
Free Bloomberg Terminal for AI Agents — open-source MCP server that researches any company in ~10 seconds.
LLMs hallucinate financial data. Other finance MCP servers return raw data from a single source — you still do the research yourself. DeepLook runs the full workflow: 10 sources in parallel, cross-referenced, with a structured bull/bear verdict. One call, ~10 seconds, no API keys needed.
⚡ Connect in 30 Seconds
- Claude.ai → Settings → Connectors → Add MCP Server
- Paste:
https://mcp.deeplook.dev/mcp - Try: "Use DeepLook to research NVIDIA"
Works with Claude Desktop, Cursor, Windsurf, or any MCP-compatible client.
What You Get
NVIDIA Corporation — $181.93 | EXPANDING / ACCELERATING
Key Signals:
🟢 Jensen Huang projects $1T AI chip revenue by 2027
🟢 Vera Rubin platform with 7 new chips in production
🔴 Earnings surprise: -55.03%
Verdict: Mega-cap AI leader with 73% revenue growth, $1T opportunity
🟢 Revenue +73.2% YoY, earnings +95.6%, $58.1B FCF
🔴 RSI 37.2 oversold, $4.42T valuation limits upside
⏳ Wait for: Q1 FY2027 earnings on 2026-05-20
Embedded structured JSON with precise metrics, peer comparison, technicals → AI clients auto-render as interactive dashboards
Features
- 10+ data sources in parallel (yfinance, news, CoinGecko, DeFiLlama, SEC EDGAR, Wikipedia, YouTube, etc.)
- Works for public stocks, crypto, and private companies
- Dual output: human-readable summary + structured JSON for AI agents
- Bull/bear verdict with catalyst timeline
- Peer comparison with financial metrics
- ~10 second research time
- Two tools:
deeplook_research(full report) anddeeplook_lookup(quick snapshot)
Supported Entity Types
Public Equity · Crypto/DeFi · Private Companies · Exchanges · VCs · Foundations
Self-Host
1. Clone and install:
git clone https://github.com/OSOJDJD/deeplook.git
cd deeplook
python3 -m venv venv && source venv/bin/activate
pip install -e .
cp .env.example .env # add at least one LLM key
2. Run as HTTP MCP server:
python -m deeplook.mcp_server --http --port 8819
3. Or add to Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"deeplook": {
"command": "/full/path/to/deeplook/venv/bin/python",
"args": ["-m", "deeplook.mcp_server"],
"cwd": "/full/path/to/deeplook",
"env": { "ANTHROPIC_API_KEY": "sk-ant-..." }
}
}
}
CLI (no MCP):
python -m deeplook "NVIDIA"
python -m deeplook "Aave"
python -m deeplook "Anthropic"
API Keys
Pick at least one LLM provider:
| Variable | Provider |
|---|---|
ANTHROPIC_API_KEY |
Claude — Haiku + Sonnet (recommended) |
OPENAI_API_KEY |
GPT-4o-mini |
GEMINI_API_KEY |
Gemini 2.0 Flash Lite |
DEEPSEEK_API_KEY |
DeepSeek Chat |
Optional (for deeper research):
| Variable | Description |
|---|---|
TAVILY_API_KEY |
Search fallback when DDG is rate-limited |
COINGECKO_API_KEY |
CoinGecko Pro for crypto data |
ROOTDATA_SKILL_KEY |
RootData for crypto project data |
Cost per report: ~$0.02–0.05 (Anthropic) · ~$0.01–0.03 (OpenAI) · ~$0.01–0.02 (Gemini) · ~$0.005–0.01 (DeepSeek)
Data Sources
| Source | Used For |
|---|---|
| yFinance | Price, financials, analyst targets, technicals |
| DuckDuckGo News | Recent signals, headlines |
| Wikipedia | Company background |
| YouTube | Earnings calls, CEO interviews |
| CoinGecko | Token price, market cap, volume |
| RootData | Crypto funding, team data |
| DefiLlama | TVL, chain metrics |
| SEC EDGAR | 10-K, 10-Q, 8-K filings |
| Finnhub | Earnings, news, sentiment |
| Website | Investor relations, product pages |
How It Works
Company Name
↓
Entity Type Router (public equity / crypto / private / exchange / VC / foundation)
↓
10 Parallel Fetchers (DDG News, yFinance, CoinGecko, SEC EDGAR, ...)
↓
3-Call LLM Pipeline: Extract (Haiku) → Judge (Sonnet) → Act (Sonnet)
↓
Structured Report + Embedded JSON
Eval
Tested across 58 companies (US mega-cap, growth stocks, crypto, pre-IPO, international, edge cases):
| Metric | Score |
|---|---|
| Overall | 3.78 / 5.0 |
| Risk detection | 4.36 / 5.0 |
| Signal quality | 3.94 / 5.0 |
| Actionability | 3.38 / 5.0 |
Eval framework ships in /eval — run it yourself, contribute ground truth data.
License
MIT — use it however you want.
Built by @OSOJDJD · Open an issue if something breaks or a report looks wrong.
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