"A guide to performing stock analysis" matching MCP servers:

A production-grade MCP server that provides tools to fetch historical stock data, company information, and technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands) via yfinance, and includes a Flask web dashboard for visual analysis.

Provides interactive stock charts and financial data tools using MCP Apps. Enables natural language queries for stock quotes, charts, financials, earnings, and market movers with a free FMP API key.

Enables data analysis and visualization operations such as loading CSV/Excel/JSON files, computing summary statistics, generating charts, and exploring datasets via SSE/HTTP.

Enables discovery of Fabric/Power BI workspaces and semantic models, execution of DAX queries, and generation of executive HTML reports with visualizations.

Enables users to create, validate, view, and export system stock-and-flow maps from a versioned JSON model through MCP-capable hosts over local stdio or hosted Streamable HTTP. Supports causal-loop and nested-level mapping while keeping submitted model content with the caller and requiring no LLM provider key.

Enables an agent to turn a paper's draft into editable Figma frames for publication-ready figures, checking every number against the paper and verifying the exported PDF.

Enables Claude to retrieve real-time financial data, extract ESG risk metrics for S&P 500 companies, and generate automated analytical visualizations through three integrated MCP tools.

Enables end-to-end analysis of Reddit discussions: fetches posts, performs sentiment and toxicity detection, generates visualizations and word clouds, and optionally builds Neo4j knowledge graphs, all through natural language orchestration.

An MCP server that answers natural-language questions over CSV, Excel, and SQL data by providing deterministic tools for loading, profiling, querying, cleaning, statistical analysis, visualization, and reporting. It enables LLMs to plan and interpret while all computation is done exactly through MCP tools.

Provides real-time stock price tracking with an interactive dashboard, fetching data via Alpha Vantage API.

A local, fully vectorized computational engine for stock market analysis that enables AI to perform factor calculation, strategy backtesting, IC analysis, and GPU-based multi-dimensional visualization using local DuckDB data.

Analyzes global and Indian stock market data via yfinance and queries local NSE BhavData CSV files using AI-generated SQL. It enables users to generate interactive HTML graphing dashboards for visual performance tracking and historical data analysis.

Enables fetching stock quotes, historical prices as CSV, and plain-text Unicode price charts via Yahoo Finance data.

Converts plain-English stock requests (e.g. "compare Apple and Microsoft YTD") into validated yfinance/matplotlib scripts, AST-checked and executed in a sandbox to produce chart PNGs. Exposes parsing, saving, execution, and history-listing tools to Claude Desktop and Claude Code through FastMCP, backed by local Ollama/DeepSeek-R1 or cloud Groq/GPT-4o models.

Exposes semiconductor wafer analysis tools including wafer maps, P-charts, and statistical plots, enabling AI assistants to visualize and analyze wafer test data.

Enables LLMs to safely execute pandas code for data analysis and generate interactive visualizations through MCP protocol.

An HTTP MCP server for exploring CSV files that masks PII before any code execution, enabling safe data analysis with local or cloud LLMs.

Enables AI assistants to pull live quotes, quarterly financials, technical indicators, FII/DII flows, option chains with IV, corporate filings, IPO data and portfolio P&L for all 8,200+ NSE and BSE listed stocks, plus a 326-ratio screener and index heatmaps. Connects either remotely over OAuth or locally via npx/uvx to Claude, ChatGPT, Cursor, VS Code, Codex and Gemini CLI.

Enables read-only analysis of FST waveform files, including signal value queries, timing measurements, event search, latency analysis, and PNG plot generation using human-readable time addressing.

Enables LLMs to discover, understand, and generate Teal R Shiny applications for clinical trial data analysis.