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Free Bloomberg Terminal for AI Agents

Free Bloomberg Terminal for AI Agents

Local
github.com/OSOJDJDDeveloper Tools12Updated 9w ago

About Free Bloomberg Terminal for AI Agents

DeepLook is a company research service for financial research and business due diligence, combining sources such as market data, filings, and news into structured analysis. It verifies data before passing it to an LLM and is available as an MCP server and REST API.

Key features

  1. 01Query multiple data sources in parallel
  2. 02Cover stocks, crypto, and private companies
  3. 03Verify data before LLM analysis
  4. 04Provide structured company research
  5. 05Offer an MCP server and REST API

Use cases

  1. 01Research a public company quickly
  2. 02Analyze crypto or private companies
  3. 03Conduct business due diligence
  4. 04Combine market data, filings, and news

MCP support

DeepLook exposes company research data and structured analytical context to MCP clients; it is also available through a REST API.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

DeepLook

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

License: AGPL-3.0 GitHub stars

What happens when you ask "Research NVIDIA"

DeepLook Dashboard

DeepLook Analysis

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

TypeExamples
Public stocksNVIDIA, Apple, Tesla, TSMC
CryptoBitcoin, Solana, Ethereum
Private companiesAnthropic, Stripe, OpenAI
VC firmsa16z, Sequoia
DefunctFTX, WeWork

Eval

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

MetricScore
Overall3.78 / 5.0
Risk detection4.36 / 5.0
Signal quality3.94 / 5.0
Actionability3.38 / 5.0

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

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.