get_stock_sentiment
Analyze stock market sentiment using AI by providing a ticker symbol to receive sentiment ratings, confidence scores, key drivers, and trading recommendations.
Instructions
Get AI-powered stock market sentiment analysis.
Args: symbol: Stock ticker symbol (e.g., AAPL, TSLA, MSFT, NVDA) include_news: Include recent news and headlines analysis include_technicals: Include technical analysis and chart patterns include_institutional: Include institutional and insider activity
Returns: Sentiment analysis with overall rating, confidence score, key drivers, and trading recommendation.
Price: $0.40
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | ||
| include_news | No | ||
| include_technicals | No | ||
| include_institutional | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- coinrailz_mcp/__init__.py:1029-1057 (handler)The implementation of the get_stock_sentiment MCP tool, which includes the tool registration decorator, parameter definition, payload construction, service call, and response serialization.
@mcp.tool() async def get_stock_sentiment( symbol: str, include_news: bool = True, include_technicals: bool = True, include_institutional: bool = True ) -> str: """ Get AI-powered stock market sentiment analysis. Args: symbol: Stock ticker symbol (e.g., AAPL, TSLA, MSFT, NVDA) include_news: Include recent news and headlines analysis include_technicals: Include technical analysis and chart patterns include_institutional: Include institutional and insider activity Returns: Sentiment analysis with overall rating, confidence score, key drivers, and trading recommendation. Price: $0.40 """ payload = { "symbol": symbol.upper(), "includeNews": include_news, "includeTechnicals": include_technicals, "includeInstitutional": include_institutional } result = await call_coinrailz_service("stock-sentiment", payload) return json.dumps(result, indent=2)