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"Using Stock Charts to Identify Stocks" matching MCP servers:

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    A Model Context Protocol server that generates lightweight ASCII charts directly in terminal environments, supporting line charts, bar charts, scatter plots, histograms, and sparklines without GUI dependencies.
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    MIT
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    An MCP server that enables AI assistants to programmatically create and manage flowcharts, ERDs, and swimlane diagrams. It provides tools for manipulating diagram structures, performing auto-layouts, and exporting to Mermaid or Markdown formats.
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    MIT
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    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.
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    MIT
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    Enables AI assistants like GitHub Copilot to access live stock market data, company information, technical analysis, news, and portfolio analytics.
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    TableCharts MCP Server gives any LLM assistant the ability to instantly convert tabular data into beautiful, hosted, interactive dashboards. Simply provide JSON rows, raw CSV text, or a public URL (Notion page, Google Sheet, or Salesforce report), and get back a shareable dashboard URL plus an embeddable iframe code — all in one tool call. Supports 6 chart types: bar, line, area, pie, scatter, a
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    MIT
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    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
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    Apache 2.0
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    An MCP server that enables AI agents to retrieve detailed GitHub Pull Request information using git commit hashes, branch names, or PR numbers. It automatically detects repositories and extracts comprehensive PR data including descriptions, labels, and reviews via the GitHub CLI.
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    ISC
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    An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.
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    MIT
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    Automates the conversion of Figma designs into TypeScript React components and integrates with GitHub to create pull requests for the generated code. It includes visual regression testing with Playwright and accessibility validation to ensure implementations match the original designs.
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    MIT
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    Enables Cursor AI to interact with Figma designs, allowing users to read design information and programmatically modify elements through natural language commands.
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