FinBrain MCP 

Requires Python 3.10+

A Model Context Protocol (MCP) server that exposes FinBrain datasets to AI clients (Claude Desktop, VS Code MCP extensions, etc.) via simple tools. Backed by the official finbrain-python SDK (v2 API).

• Package name: finbrain-mcp

• CLI entrypoint: finbrain-mcp

• Documentation: finbrain.tech/integrations/mcp

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Features

AI-Powered Price Predictions

Access FinBrain's machine learning price forecasts with daily (10-day) and monthly (12-month) horizons. Includes mean predictions with 95% confidence intervals.

News & Sentiment Analysis

Browse recent news articles for any ticker, or track aggregated daily sentiment scores over time. Screen news across all tracked stocks.

Alternative Data

• LinkedIn Metrics — Employee count and follower trends as company health indicators
• App Store Ratings — Mobile app performance data for consumer-facing companies
• Options Flow — Put/call ratios and volume to gauge market positioning

Institutional & Insider Activity

• US Congress Trades — Stock transactions disclosed by House representatives and Senators
• Insider Transactions — SEC Form 4 filings showing executive buys and sells
• Analyst Ratings — Wall Street coverage and price target changes

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What you get

• ⚡️ Local MCP server (no proxying) using your own FinBrain API key

• 🧰 Tools (JSON by default, CSV optional) with paging

  • health

  • available_markets, available_tickers, available_regions

  • predictions_by_market, predictions_by_ticker

  • news_by_ticker, news_sentiment_by_ticker

  • app_ratings_by_ticker

  • analyst_ratings_by_ticker

  • house_trades_by_ticker, senate_trades_by_ticker

  • insider_transactions_by_ticker

  • linkedin_metrics_by_ticker

  • options_put_call

  • recent_news, recent_analyst_ratings

  • screener_sentiment, screener_analyst_ratings, screener_news

  • screener_insider_trading, screener_house_trades, screener_senate_trades

  • screener_put_call_ratio, screener_linkedin, screener_app_ratings

• 🧹 Consistent, model-friendly shapes (we normalize raw API responses)

• 🔑 Multiple ways to provide your API key: env var, file

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Install

Option A — Standard install (pip)

# macOS / Linux / Windows
pip install --upgrade finbrain-mcp

Option B — Dev install (editable)

# from repo root
python -m venv .venv
source .venv/bin/activate # Windows: .\.venv\Scripts\activate
pip install -e ".[dev]"

Keep pip (prod) and your venv (dev) separate to avoid path mix-ups.

Option C — Docker

# Build the image
docker build -t finbrain-mcp:latest .

# Run with your API key
docker run --rm -e FINBRAIN_API_KEY="YOUR_KEY" finbrain-mcp:latest

See DOCKER.md for detailed Docker usage instructions.

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Configure your FinBrain API key

A) In your MCP client config (recommended / most reliable)

Put the key directly in the MCP server entry your client uses (Claude Desktop or a VS Code MCP extension). This guarantees the launched server sees it, even if system env vars aren’t picked up.

Claude Desktop (pip install)

{
  "mcpServers": {
    "finbrain": {
      "command": "finbrain-mcp",
      "env": { "FINBRAIN_API_KEY": "YOUR_KEY" }
    }
  }
}

B) Environment variable

This works too, but note you must restart the client after setting it so the new value is inherited.

# macOS/Linux
export FINBRAIN_API_KEY="YOUR_KEY"

# Windows (PowerShell, current session)
$env:FINBRAIN_API_KEY="YOUR_KEY"

# Windows (persistent for new processes)
setx FINBRAIN_API_KEY "YOUR_KEY"
# then fully quit and reopen your MCP client (e.g., Claude Desktop)

Tip: If the env var route doesn’t seem to work (common on Windows if the client was already running), use the config JSON env method above—it’s more deterministic.

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Run the server

Note: You typically don’t need to run the server manually—your MCP client (Claude/VS Code) starts it automatically. Use the commands below only for manual checks or debugging.

• If installed (pip):

  finbrain-mcp

• From a dev venv:

  python -m finbrain_mcp.server

Quick health check without an MCP client:

python - <<'PY'
import json
from finbrain_mcp.tools.health import health
print(json.dumps(health(), indent=2))
PY

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Connect an AI client

No manual start needed: Claude Desktop and VS Code will launch the MCP server for you based on your config. You only need to run finbrain-mcp yourself for quick sanity checks or debugging.

Claude Desktop

Edit your config:

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Linux: ~/.config/Claude/claude_desktop_config.json

Pip install (published package):

{
  "mcpServers": {
    "finbrain": {
      "command": "finbrain-mcp",
      "env": { "FINBRAIN_API_KEY": "YOUR_KEY" }
    }
  }
}

macOS tip (full path):

If "command": "finbrain-mcp" doesn’t work, find the absolute path and use that instead.

which finbrain-mcp    # macOS/Linux
# (Windows: where finbrain-mcp)

Claude config with full path (macOS example):

{
  "mcpServers": {
    "finbrain": {
      "command": "/full/path/to/finbrain-mcp",
      "env": { "FINBRAIN_API_KEY": "YOUR_KEY" }
    }
  }
}

Dev venv (run the module explicitly):

{
  "mcpServers": {
    "finbrain-dev": {
      "command": "C:\\Users\\you\\path\\to\\repo\\.venv\\Scripts\\python.exe",
      "args": ["-m", "finbrain_mcp.server"],
      "env": { "FINBRAIN_API_KEY": "YOUR_KEY" }
    }
  }
}

Docker:

{
  "mcpServers": {
    "finbrain": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "finbrain-mcp:latest"],
      "env": { "FINBRAIN_API_KEY": "YOUR_KEY" }
    }
  }
}

After editing, quit & reopen Claude.

VS Code (MCP)

1. Open the Command Palette → “MCP: Open User Configuration”.
   This opens your mcp.json (user profile).

2. Add the server under the servers key:

   {
     "servers": {
       "finbrain": {
         "command": "finbrain-mcp",
         "env": { "FINBRAIN_API_KEY": "YOUR_KEY" }
       }
     }
   }
   

3. In Copilot Chat, enable Agent Mode to use MCP tools.

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What can you ask the agent?

You don’t need to know tool names—just ask in plain English. Examples:

• Predictions

  • “Get FinBrain’s daily predictions for AMZN.”
  • “Show monthly predictions (12-month horizon) for AMZN.”
  • “Get market-wide daily predictions for S&P 500 tickers.”

• News

  • “Get recent news articles for AMZN.”
  • “What’s the news sentiment for AMZN from 2025-01-01 to 2025-03-31 (limit 50)?”
  • “Show me the latest news across all S&P 500 stocks.”

• App ratings

  • “Fetch app store ratings for AMZN between 2025-01-01 and 2025-06-30.”

• Analyst ratings

  • “List analyst ratings for AMZN in Q1 2025.”

• Congressional trades

  • “Show recent House trades involving AMZN.”
  • “Show recent Senate trades involving META.”

• Insider transactions

  • “Recent insider transactions for AMZN?”

• LinkedIn metrics

  • “Get LinkedIn employee & follower counts for AMZN (last 12 months).”

• Options (put/call)

  • “What’s the put/call ratio for AMZN over the last 60 days?”

• Screeners (cross-ticker)

  • “Screen sentiment across S&P 500 stocks.”
  • “Show the latest analyst ratings across all stocks.”
  • “Screen insider trades across all tickers (limit 50).”
  • “Screen LinkedIn data for US region stocks.”

• Availability

  • “Which markets are available?”
  • “List tickers in the daily predictions universe.”
  • “Show available regions and their markets.”

Notes

• Date format: YYYY-MM-DD.
• Time-series endpoints return the most recent N points by default—say “limit 200” to get more.
• Predictions horizon: daily (10-day) or monthly (12-month).
• Say “as CSV” to receive CSV instead of JSON.
• No need to specify a market—just use the ticker symbol directly.

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Development

# setup
python -m venv .venv
source .venv/bin/activate # Windows: .\.venv\Scripts\activate
pip install -e ".[dev]"  # run tests pytest -q

Project structure (high level)

finbrain-mcp
├─ README.md
├─ pyproject.toml
├─ LICENSE
├─ .github/
├─ examples/
├─ src/
│  └─ finbrain_mcp/
│     ├─ __init__.py
│     ├─ server.py                # MCP server entrypoint
│     ├─ registry.py              # FastMCP instance
│     ├─ client_adapter.py        # wraps finbrain-python; calls normalizers
│     ├─ auth.py                  # resolves API key (env var)
│     ├─ settings.py              # tweakable defaults (e.g., series limits)
│     ├─ utils.py                 # helpers (latest_slice, CSV, DF->records)
│     ├─ normalizers/             # endpoint-specific shapers
│     └─ tools/                   # MCP tool functions (registered & testable)
└─ tests/                         # pytest suite with a fake SDK

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Troubleshooting

• ENOENT (can’t start server)

  • Wrong path in client config. Use the venv’s exact path:

    • …\.venv\Scripts\python.exe + ["-m","finbrain_mcp.server"], or

    • …\.venv\Scripts\finbrain-mcp.exe

• FinBrain API key not configured

  • Put FINBRAIN_API_KEY in the client’s env block or

  • setx FINBRAIN_API_KEY "YOUR_KEY" and fully restart the client.

• Mixing dev & prod installs

  • Keep pip (prod) and venv (dev) separate.

  • In configs, point to one or the other—not both.

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License

MIT (see LICENSE).

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Acknowledgements

• Built on Model Context Protocol and FastMCP.

• Uses the official finbrain-python SDK.

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© 2026 FinBrain Technologies — Built with ❤️ for the quant community.