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"WhyLabs AI Observability Platform" 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 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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    Connect AI assistants to Victron Energy systems to read real-time solar, battery, grid, and inverter data from your local network via Modbus TCP or MQTT.
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    MIT
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    AI agent for Italian energy tariff comparison. Analyzes electricity and gas bills, compares 44+ offers from 13 providers, estimates savings with full ARERA regulated cost breakdown
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    MIT
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    An unofficial MCP (Model Context Protocol) server that exposes a Vaillant heat pump's data to AI assistants like Claude. Query outdoor and room temperatures, hot water status, energy consumption, COP estimates, schedules, and diagnostics through natural conversation.
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    MIT
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    Provides AI agents with read access to Trane Tracer SC+ building automation systems for inventory and telemetry retrieval.
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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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    MCP server enabling AI assistants to automatically perform carbon footprint modeling, product queries, and emission analysis via the Carbonstop Cloud API.
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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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    Enables AI agents to interact with a user's Fill-Rite fuel management system, with token refresh handling for continuous access.
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    9
    MIT
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    An MCP server that enables AI assistants to look up solar permitting authorities, estimate solar production via PVWatts, and retrieve irradiance data. It streamlines the creation of solar-aware workflows by integrating industry-standard APIs like NREL.
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    MIT
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    Enables AI agents to operate a US power-grid data pipeline – ingest, plan, run, and monitor datasets like CAISO, ERCOT, and EIA-930 through MCP tools that mirror the CLI.
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    Apache 2.0