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Research stocks with financial data, analytics, DCF, AI commentary, and portfolio tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "stockdata-mcp" yet — see the docs or source repo.
Using data from this MCP tool, summarize AAPL’s key financial metrics, recent performance, and main analytical takeaways with a brief explanation.
A summary with raw financial data highlights, key metric interpretation, and concise research conclusions.
Run a DCF valuation for MSFT, and list the core assumptions, valuation result, and key uncertainties to watch.
A valuation summary including the DCF result, main assumptions, and risk notes.
Using this tool, compile a research overview of my portfolio, including key holding-level data, analytical highlights, and overall observations.
A portfolio-level summary with aggregated data, analytical commentary, and overall research insights.
Researchers or data analysts can use it to retrieve raw financial data and combine it with derived analytics for faster investment research. It fits workflows that need both data access and analytical conclusions.
When a user needs a DCF valuation and AI commentary for a stock, this tool serves as a unified entry point for data and analysis. It is useful for initial valuation write-ups and viewpoint synthesis.
When tracking multiple stocks, users can use its portfolio management capability to review research information in one place. It is suitable for organizing holding-related data, analytical highlights, and overall observations.
It is an MCP server for stock research with 52 tools across FMP and Qualtrim backends. Its capabilities include raw financial data, derived analytics, DCF, AI commentary, and portfolio management.
Yes. The description explicitly says it handles caching, API budgeting, and credential management. This means it addresses not only research features but also cost and access management.
The provided material does not include installation steps, but it does mention credential management, so access credentials are likely involved. For exact setup steps, runtime requirements, and key configuration, see the source repository.
Research stocks with real-time data, fundamental analysis, comparisons, and record management.
Access stock quotes, historical data, and technical indicators through MCP.
Access real-time stock data, fundamentals, comparisons, and market summaries for AI assistants.
Access live market data, indicators, financial news, and Python-based analysis.
Aggregate stock data and execute trades for research and analysis.
Access multi-market stock data and support AI-assisted financial analysis.