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    Enables AI assistants to fully interact with console applications, monitor output, detect errors, and automate terminal workflows across multiple sessions. Similar to how Playwright works for web browsers but for command-line interfaces.
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    MIT
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    Downloads your entire Search Console dataset into a local SQLite database, then gives your LLM a pre-built SQL query library for every standard SEO analysis type, with context available for your LLM to perform any SQL query to answer your questions and analyse for you.
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    Apache 2.0
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    An MCP server that gives AI assistants tools to analyze Salesforce Apex debug logs for performance bottlenecks, slow methods, and governor limit usage.
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    BSD 3-Clause
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    Google Search Console for Claude, Cursor & any MCP client — one sign-in, 30 seconds, no Google Cloud project. Six read-only tools (search analytics incl. Google Discover, period comparison with computed deltas, batch URL inspection, sitemaps) plus five built-in SEO analyses as slash-commands. Tokens stay on your machine. MIT.
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    MIT
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    MCP server for VibeCheck bug reports. Gives AI assistants direct access to screen recordings, console logs, network requests, user actions, and web vitals — right inside your editor.
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    MIT
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    Enables AI agents to search and analyze SAP Commerce Cloud (CCv2) runtime logs via OpenSearch Dashboards using a session cookie, providing read-only tools for full-text search and error aggregation.
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    MIT
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    An MCP server for rendering common ECharts visualizations from structured tool arguments, exposing per-chart opener tools that map to independent HTML app resources.
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    Provides AI assistants with Safari browser automation and developer tools access, enabling LLMs to control Safari, access console logs, monitor network activity, and perform browser automation tasks.
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    A server that provides access to Google Search Console data through the Model Context Protocol, allowing users to retrieve and analyze search analytics data with customizable dimensions and reporting periods.
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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 querying Azure Application Insights logs by order number through natural language. Provides secure access to Azure Monitor logs with comprehensive error handling and rate limiting.
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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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