DeepLook

LLMs hallucinate financial data. DeepLook gives them real numbers instead.

What happens when you ask "Research NVIDIA"

DeepLook provides structured context — real-time data from 8+ APIs combined with analytical instructions that makes better output. The result:

• Accurate, up-to-date information — not hallucinated numbers
• Clear at a glance — financials, peers, technicals, news in one view
• Better AI output — because good context drives good analysis

Works for financial research, business due diligence, or any use case where you need to understand a company fast.

Under the hood

• 10+ sources, one call — queries fan out in parallel across market data, filings, news, and more
• Rules before reasoning — data is verified before the LLM ever sees it, which is what keeps hallucination down
• Eval-tested prompts — changes get scored before they ship, not just shipped on vibes
• Agent-ready — available as both an MCP server and a REST API

Get started

Claude.ai

1. Go to Settings → Connectors → Add MCP
2. Paste: https://mcp.deeplook.dev/mcp
3. Start a new chat and ask: "Research NVIDIA"

Other MCP clients (Cursor, VS Code, Windsurf, Claude Desktop)

{
  "mcpServers": {
    "deeplook": {
      "url": "https://mcp.deeplook.dev/mcp"
    }
  }
}

Claude Code

claude mcp add --transport http deeplook https://mcp.deeplook.dev/mcp

Self-host

git clone https://github.com/OSOJDJD/deeplook.git
cd deeplook
pip install -r requirements.txt
python -m deeplook.mcp_server

What it covers

Eval

Tested across 58 companies, scored on accuracy, hallucination, and usefulness:

From an earlier pipeline version — re-run pending. Eval harness in /deeplook/eval.

Extend DeepLook

DeepLook covers the basics. If you need data it doesn't have yet — a new market, a new data source, a new analysis rule — you can add it. See CONTRIBUTING.md.

Roadmap

• More query tools — news, peers, financials, calendar as standalone lookups
• Broader client support — Cursor, ChatGPT, VS Code, Windsurf, Claude Code
• Deeper context — more analytical conditions, entity-specific instructions
• Community contributions — new data sources, custom analysis rules

License

AGPL-3.0

Built by @OSOJDJD