FinBrain MCP

@ahmetsbilgin

About FinBrain MCP

Access institutional-grade alternative financial data directly in your LLM workflows.

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "finbrain-mcp": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

Tools

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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is FinBrain MCP?

FinBrain MCP is a Model Context Protocol server that exposes FinBrain datasets to AI clients like Claude Desktop and VS Code MCP extensions. It provides tools for financial data including price predictions, news sentiment, alternative data, and institutional/insider activity. Requires Python 3.10+ and a FinBrain API key.

How to use FinBrain MCP?

Install via pip install --upgrade finbrain-mcp, configure your FinBrain API key in the MCP client’s config (Claude Desktop or VS Code), then ask natural-language questions. The client launches the server automatically; no manual start needed.

Key features of FinBrain MCP

AI-powered price predictions with 10-day and 12-month horizons
News sentiment analysis with aggregated scores per ticker
Alternative data: LinkedIn metrics, App Store ratings, Options flow
Institutional activity: US Congress trades, insider transactions, analyst ratings
Tools return JSON by default, CSV on request, with paging support
Local MCP server using your own API key (no cloud proxying)

Use cases of FinBrain MCP

Get daily or monthly price forecasts for any ticker
Track aggregate news sentiment and market mood shifts
Monitor insider transactions and congressional stock trades
Fetch analyst ratings and price target changes
Retrieve alternative indicators like LinkedIn follower trends and put/call ratios

FAQ from FinBrain MCP

What are the runtime requirements for FinBrain MCP?

Python 3.10+ is required. Install the package via pip; a FinBrain API key is mandatory.

How do I provide my FinBrain API key?

Set FINBRAIN_API_KEY in the MCP client’s env block (recommended) or as an environment variable. Restart the client after setting the env var.

What endpoints are available?

Tools include predictions_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, and basic ones like health, available_markets, and available_tickers.

Can I get CSV output instead of JSON?

Yes – simply say “as CSV” in your request, and the tool will return CSV formatted data.

Is data proxied or stored locally?

The MCP server runs locally and directly accesses FinBrain’s API with your own key. No proxying; data is fetched live from FinBrain’s cloud.

Frequently asked questions

What are the runtime requirements for FinBrain MCP?

Python 3.10+ is required. Install the package via pip; a FinBrain API key is mandatory.

How do I provide my FinBrain API key?

Set `FINBRAIN_API_KEY` in the MCP client’s `env` block (recommended) or as an environment variable. Restart the client after setting the env var.

What endpoints are available?

Tools include `predictions_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`, and basic ones like `health`, `available_markets`, and `available_tickers`.

Can I get CSV output instead of JSON?

Yes – simply say “as CSV” in your request, and the tool will return CSV formatted data.

Is data proxied or stored locally?

The MCP server runs locally and directly accesses FinBrain’s API with your own key. No proxying; data is fetched live from FinBrain’s cloud.

Comments

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