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.
Get started
Claude.ai
- Go to Settings → Connectors → Add MCP
- Paste:
https://mcp.deeplook.dev/mcp - 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
| Type | Examples |
|---|---|
| Public stocks | NVIDIA, Apple, Tesla, TSMC |
| Crypto | Bitcoin, Solana, Ethereum |
| Private companies | Anthropic, Stripe, OpenAI |
| VC firms | a16z, Sequoia |
| Defunct | FTX, WeWork |
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
Built by @OSOJDJD
Metadata
Release files for deeplook-research 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| deeplook_research-0.1.0.tar.gz | 113.2 kB | Without provenance | Details for deeplook_research-0.1.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| deeplook_research-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for deeplook_research-0.1.0-py3-none-any.whl |
Total release size: 238.4 kB
Release files / deeplook_research-0.1.0.tar.gz
| Download URL | deeplook_research-0.1.0.tar.gz |
|---|---|
| Size | 113.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.3
|
Release files / deeplook_research-0.1.0-py3-none-any.whl
| Download URL | deeplook_research-0.1.0-py3-none-any.whl |
|---|---|
| Size | 125.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.3
|
Security for deeplook-research 0.1.0
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