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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.
    1
    Apache 2.0
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    An MCP server that exposes Home Assistant's REST and WebSocket APIs as 182 tools, enabling clients like Claude Code to control lights, climate, media, automations, and more. It also provides a status dashboard and supports running as a Home Assistant app or container.
    AGPL 3.0
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    MCP server for querying ENTSO-E Transparency Platform data including actual load, generation per type, cross-border flows, and installed capacity.
    3
    MIT
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    MCP server that provides live facility data access and operational tools, enabling the AI agent to query sensor readings, HVAC status, energy usage, alerts, and execute confirmed maintenance actions.
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    A Model Context Protocol server that connects Claude to your own Kia vehicle through the Kia Owners API, enabling vehicle status, location, odometer, EV charge state, and confirm-gated commands for doors, climate, and charging.
    16
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    MIT
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    Read-only MCP server for Home Assistant that computes statistical summaries, trends, and session detection from recorded sensor history, returning compact tables instead of raw data so small local models can answer questions about energy usage, climate, and appliance activity without token-heavy arithmetic.
    10
    Apache 2.0
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    An MCP server for US electricity demand and weather data that provides tools, resources, and prompts to query hourly demand, daily demand, peak demand, and demand vs temperature across regions, all running locally with a bundled SQLite database.
    6
    MIT
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    Enables AI assistants to control a Homey Pro smart home hub, create automation flows from natural language, and query device and energy history.
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    MIT
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    Enables discovery and control of Philips Hue lighting devices via a local bridge using the CLIP v2 API, without any cloud dependency.
    10
    MIT
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    The PGIQ Rating as agent tools: where to site or schedule a large electricity load across 67 global power markets. Wraps a read-only REST API returning reasoned, cited ratings across access, availability, cost, momentum, and carbon, plus live grid snapshots and cheapest-window scheduling.
    6
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    MIT
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    A lean Model Context Protocol (MCP) server that gives AI assistants like Claude structured access to a SolarEdge PV installation via the official SolarEdge Monitoring API.
    4
    MIT
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    Description: Data-center, power & gas intelligence MCP server. 33 tools covering 21,000+ data-center facilities (170+ countries), 232 US power markets scored by the DC Hub Power Index (DCPI), 2,000+ tracked M\&A deals, ISO grid telemetry (PJM, ERCOT, CAISO, MISO, SPP, NYISO), fiber routes, energy pricing. License: Free to cite (CC-BY-4.0). Existing distribution: In the official MCP registry; indexe
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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.
    4
    MIT
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    A Model Context Protocol server providing free financial market data across 13 categories with 83 no-key tools, enabling access to stocks, indices, options, forex, crypto, energy, prediction markets, and more.
    83
    MIT
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    Enables AI assistants to query Tesla vehicle data and analytics from a TeslaMate PostgreSQL database, including battery health, driving statistics, charging sessions, and custom SQL queries.
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    MIT
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    Fits a lithium-ion full-cell dual-water-tank model from charge/discharge voltage-capacity data for battery aging mechanism identification, with parameter fitting, batch processing, and MCP server integration.
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    Apache 2.0
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    Read-only MCP server integrating with the Victron VRM API to monitor solar systems, batteries, alarms, and more.
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    MIT
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    Enables querying optimal control values (angle for blowers, control for pumps) to minimize power given a required flow rate, using machine learning models trained on pump/fan operation data.
    2
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