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"A server for orchestrating and validating AI agents" matching MCP servers:

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    Carbon-aware MCP scheduler that defers "do it later" / "by tomorrow" / "overnight" LLM tasks to the cleanest electricity-grid hour inside a deadline — 40-70% lower CO2 per task, 50% cheaper via Anthropic/OpenAI Batch APIs. 9 MCP tools, per-task carbon receipts to local SQLite, Apache-2.0.
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    Apache 2.0
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    Enables parsing, analysis, and export of power measurement data from Monsoon Power Monitor PT5 files, including summary statistics, sample retrieval, trend analysis, and CSV export.
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    MIT
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    Provides real-time electricity grid data including CO2 intensity, power mix, and wholesale prices, plus optimal green time windows for energy-intensive AI tasks. Supports UK, Germany, and global regions with optional API keys.
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    MIT
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    Connects AI agents to energy infrastructure with 30+ tools for managing sites, assets, dispatch, settlements, compliance, and carbon tracking.
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    MIT
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    MCP server for the German energy market master data register (MaStR), enabling querying of energy units, actors, grid connections, and more via 21 SOAP and public tools.
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    MIT
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    Enables natural language control of Loxone smart home systems, including lighting, audio, climate, and environmental monitoring, through MCP-compatible clients.
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    AGPL 3.0
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    A physics engine for liquid-cooled GPU systems, exposed as an AI-callable MCP server. Enables thermal analysis, coolant comparison, flow optimization, and rack-level sizing via natural language queries.
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    MIT
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    Enables access to Fronius solar inverter data via the MCP protocol, allowing real-time monitoring of energy production, consumption, and battery storage through natural language.
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    MIT
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    Provides AI agents with structured access to the U.S. EIA Open Data API for energy data including power plants, operations, fuel prices, projections, and state CO2 emissions.
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    MIT
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    A powerful MCP server that enables AI assistants like Claude to access, analyze, and optimize solar energy data from FoxESS inverters.
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    MIT
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    Enables calculation of the most economical fuel (gasoline, ethanol, or natural gas) based on prices and vehicle consumption, following the 70% rule for ethanol and 60% for GNV.
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