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    AI-native open-source 2D game engine whose MCP server exposes every editor operation to coding agents as typed commands, so an agent can build, run, and verify a game inside the editor. It runs locally on macOS, Windows, and Linux with no cloud service or API key.
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    MIT
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    Exposes Mireye Earth's geospatial data API as MCP tools (mireye_ask and mireye_fetch) for querying location-based information like elevation, flood zones, and wildfire risk for US places.
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    MIT
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    MCP server for AI agent deployment health — gateway status, CPU/memory/swap, recent errors from journalctl/dmesg, skill registry integrity, upgrade outcomes, cron + disk usage. Each component gets HEALTHY/DEGRADED/CRITICAL classification with overall rollup.
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    MIT
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    MCP server to read daily activity, sleep, heart rate, and body metrics from Google Health API, allowing AI assistants like Claude to access your health data. Optionally syncs health metrics to an Obsidian vault.
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    MIT
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    An MCP server that enables AI agents to query Apple Health data (190+ metrics) in natural language, including trends, comparisons, and structured exports.
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    MIT
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    A local-first MCP server that enables AI agents to read user-authorized Google Health API v4 data from Fitbit, Pixel Watch, and partners via OAuth, with tokens never leaving the machine.
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    MIT
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    An MCP server for querying and analyzing Apache Incubator podling health reports. It provides tools to list, search, and compare podling metrics across different time windows from Apache's health report Markdown files.
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    MIT
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    An MCP server that performs comprehensive health checks on project dependencies for JavaScript and Python projects, detecting outdated packages and fetching changelogs.
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    Enables Claude Desktop to access and analyze Garmin wearable health data including sleep, HRV, Body Battery, and activity metrics. Users can query their health trends, track recovery, and generate interactive HTML dashboards using natural language.
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    MIT
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    Metis — an open-source (AGPL-3.0), local-first MCP server / research companion. It answers from the user's own indexed PDF library with page-level citations (and flags what it can't find rather than inventing), keeps all data on the user's machine, and routes requests to 34 specialist agents.
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    AGPL 3.0