"An analysis of financial or statistical charts" matching MCP servers:

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.

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

Enables AI agents to discover, retrieve, compare, and analyze trusted macroeconomic statistics from central banks and international organizations, resolving concepts to official series with provenance and validation.

Enables MCP-capable agents to profile datasets and render CSV/TSV/TXT/JSON data into standalone interactive ECharts HTML charts fully offline, returning the HTML artifact plus rendering statistics in the same response.

A modern Python MCP server for rendering customizable charts (line, bar, pie, scatter, area, combined dashboards) with Plotly, supporting export to PNG, SVG, and base64, and terminal charts with ANSI output.

Provides financial stock analysis for U.S. public companies via SEC EDGAR APIs, offering tools to resolve companies, retrieve filings, calculate profitability metrics, compare periods, detect red flags, and get audit evidence, all without API keys.

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

Provides MCP tools to search, retrieve, and answer questions from the public Open Finance Brasil Confluence documentation using BM25-based local indexing.

Provides actionable financial intelligence tools for AI agents including insider buying signals, earnings IV plays, market pulse, stock analysis, and options strategies via free public data sources.

FastMCP server for image analysis providing OCR, metadata, and EXIF extraction.

Records and scores prospective equity earnings predictions made by AI agents, allowing comparison of different agent configurations.

Public MCP research for AI finance and trading agents: funding, bank risk and liquidity evidence. Companion SDK: persistent Python MCP sessions and Python/JS source-status streams. Setup: https://beepboop2025.github.io/financial-evidence-skills/distribution/ai-trading-tools.html. No data API key. Keep source dates, rights and gaps. No live quotes or execution.

Enables persona-configurable financial research and answers questions about 24 US-listed companies across three sectors, grounded in a SQLite database via MCP tools.

Provides comprehensive data analysis utilities including statistical functions, probability distributions, and data processing tools through natural language.

Enables Hermes agents to build and run auditable startup financial models, validate inputs, execute linked-statement scenarios, verify results, summarize outcomes, and perform sensitivity analyses through six finance tools.

MCP server that provides AI assistants access to stock market data including financial statements, stock prices, and market news through a Model Context Protocol interface.

Citation-first MCP server for official Indonesian financial data from IDX, BPS, and KSEI, providing tools to access company profiles, financial reports, statistical tables, and investor demographics.

Enables LLMs to retrieve, analyze, and visualize stock prices and financial report data for quantitative trading research and investment analysis. Provides real-time and historical stock data, financial statement analysis, key metric calculations, and trading signal visualization.

Enables querying options chains and estimating gamma exposure for ETFs and liquid underlyings, including proxy gamma calculations and historical gamma snapshots.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

An MCP server that integrates SEC EDGAR, FRED, and Polygon.io financial data with OAuth 2.1 security and a citation-grounded 10-K extractor powered by Claude Sonnet 4.5.

Institutional Financial Intelligence MCP Server for Claude Desktop & AI Agents. Sub-second market data, technical indicators, SEC EDGAR 10-K parsing, and local SQLite backtesting.

Provides access to Chinese mainland financial data including A-stock quotes, financial statements, industry analysis, and macroeconomics through 42 MCP tools, with automatic data source fallback and no API key required.

Enables natural-language stock comparison queries that produce PNG charts and financial metrics such as returns, volatility, and drawdowns via MCP tools.

Enables zero-dependency statistical analysis of numerical and time-series streams, flagging outliers via standard Z-score, modified median absolute deviation (MAD), and Tukey IQR fences. Also supports trend forecasting with Holt linear smoothing, multivariate gradient-descent regression, Welch's t-test hypothesis testing, and Power Iteration PCA dimensionality reduction through a native MCP stdio interface.

Provides derived financial intelligence for AI agents, including insider activity analysis, earnings surprises, institutional moves, stock screening with a proprietary composite value score, and macro indicators.

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

An MCP server that connects AI assistants to YNAB budgets, enabling natural language queries about finances backed by full API coverage and built-in YNAB methodology knowledge.

An MCP server that provides six code review tools for use with Claude Code or any MCP client. Each tool reads a file from disk, pairs its contents with a structured review prompt, and returns the bundle for the LLM to evaluate.

Enables agents to detect outliers in numerical streams using multiple robust statistical strategies and to perform related analytics such as forecasting, regression, hypothesis testing, and dimensionality reduction via MCP.