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    Zero-auth hosted MCP for AI builders: optimize/convert images (WebP/AVIF/JPEG), paste-ready SVG QR codes, plus cited Cleanor Labs data on device storage capacity and next-gen image-format savings. No API key or signup.
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    MIT
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    An open-source MCP server for live Apple App Store competitor research, enabling AI agents to search apps, fetch metadata, compare competitors, and retrieve reviews and top charts as structured JSON.
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    MIT
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    Provides live stock data and market news for financial analysis via any MCP-compatible AI app.
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    MIT
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    Provides AI agents with access to Hacker News data including top stories, story details, comment threads, and full-text search for content research and trend monitoring.
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    MIT
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    MCP server for local Zotero on Windows and WSL, enabling read access to items, collections, annotations, and notes via the Local API, and safe write operations through a plan/apply workflow with Web API verification.
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    MIT
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    Simulate hundreds of AI agents to predict how communities react to events and policies. Upload any document (PDF, Markdown, text) and DeepMiro spawns a diverse swarm of AI agents that debate, share, and form opinions — then delivers a calibrated prediction report. Free and open-source.
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    AGPL 3.0
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    40-tool colour intelligence API for AI agents. Cultural risk, archive-verified colour naming, palette generation, WCAG accessibility, material physics, ecommerce copy, and image generation briefs. 16,682+ named colours across 43 cultural archives.
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    MIT
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    Open-source Model Context Protocol server for iOS App Store market research with four read-only tools for app discovery, revenue/download estimates, ASO keyword research, and review analysis.
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    MIT
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    Provides read-only access to the Handigraphs Stats API v1, enabling discovery of sports resources and querying of protected sports statistics.
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    Apache 2.0
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    An MCP server that provides comprehensive market intelligence by analyzing data from both the Apple App Store and Google Play Store, enabling users to research apps, track market trends, study competitors, and understand user feedback across mobile marketplaces.
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    MIT
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    This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.
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    MIT