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MCP tools

MCP Connectors

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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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    9 npm
    MIT
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    MCP server for the entire Shelly smart-home ecosystem, enabling local-first control, automation, and energy monitoring of Shelly devices with cloud fallback.
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    32 PyPI
    MIT
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    MCP server for Kardashev Labs US grid data, giving agents live LMP, carbon intensity, load, curtailment, interconnection queues, ERCOT large loads, and the public RT-DA spread forecast track record. Requires no API key.
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    37 npm
    MIT
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    Enables AI assistants to create, edit, and simulate EnergyPlus building energy models via natural language. Supports schema exploration, model editing, simulation execution, and documentation search.
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    MIT
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    Hourly electricity grid carbon intensity (gCO2eq/kWh) for 45 zones across Europe, the US and Great Britain, computed from ENTSO-E, EIA and NESO generation data. Four read-only tools; every value carries its interval timestamp and how stale it is. No API key, no account.
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    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.
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    32 npm
    MIT
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    Generates and verifies zero-knowledge proofs locally for Energy Web Verified Compute, keeping private meter readings and blinding salts on the user's machine while enabling agents to submit verifiable proof packages.
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    185 npm
    MIT
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    Enables querying and downloading Equatorial Energia invoices via MCP tools, supporting listing consumer units, listing invoices, and downloading second-copy PDFs with local data extraction.
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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.
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    3 npm
    Apache 2.0
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    Enables MCP clients to search Colombian energy and gas resolutions and legal opinions, read them in chunks or by internal search, and explore each rule's regulatory graph with validity computed from the texts themselves.
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    MIT
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    Enables natural-language interaction with Ladybug Tools for building performance simulation, 3D modeling, and data visualization, allowing agents to perform complex environmental analysis tasks without CAD software.
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    36 PyPI
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    GPL 3.0
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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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    Connects Claude and other MCP clients to myGEKKO building controllers via the official Query API, enabling natural-language queries and control of smart-home devices. It is read-only by default, with write actions available only through explicit opt-in and safety policies.
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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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    Enables users to search generator guidance, check connector and receptacle compatibility, and calculate cord sizing based on distance and voltage drop.
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    MIT
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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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    MCP server that connects to Felicity Solar cloud battery data, allowing natural language queries about battery status, cell voltages, and charge state.
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    42 npm
    MIT
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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.
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    MIT