"Developing a FHIR-Based Digital Health Application with EHR, PHR, and Phenotyping Frameworks" matching MCP servers:

An AI-powered EV Digital Twin platform for battery health monitoring, predictive maintenance, fleet analytics, and intelligent decision support using MCP tools for SOH prediction and RUL estimation.

Provides data-quality validation and anomaly scoring tools so an AI agent can decide whether to dispatch a field technician, while refusing to act on untrusted data.

Exposes EnergyPlus simulation control (run, parse metrics, edit schedules) as LLM tools for autonomous building energy optimization.

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.

Enables Alexa+ to answer spoken questions about a home's energy use and act on them through seven voice-first tools, covering live device status, usage and cost reports, waste detection, savings recommendations, automation listing, device control, and confirmed energy-plan application.

MCP server for controlling and monitoring Digital Loggers Power Switch Pro devices, enabling outlet control, power monitoring, and device management through natural language.

Provides LLMs with access to news and information about renewable energy projects and planning in Germany via the Nefino API, enabling location-based queries with topic and date filtering.

MCP server for EG4 solar inverters, enabling real-time monitoring, performance analysis, battery health, alerts, maintenance insights, and historical data via natural language.

An MCP (Model Context Protocol) server that exposes Polestar 2 vehicle data to AI assistants like Claude. Query your car's battery status, vehicle info, and health data through natural conversation.

A Model Context Protocol (MCP) server that provides comprehensive access to Tigo Energy solar system data and analytics. It enables AI assistants to interact with your Tigo solar monitoring system to retrieve production data, performance metrics, system health information, and maintenance insights.

Enables local control of an installed PVsystCLI 8 on Windows, letting a client check license quota, list workspace projects and variants, build SFI definitions, run existing project/variant simulations, convert staged weather data into MET files, and summarize numeric result columns. It exposes only seven typed tools with filename-based access to fixed workspace folders, and never overwrites existing output files.

A TypeScript-based Model Context Protocol (MCP) server with web interface for calculating CO2 emissions using the Climatiq API.

MCP server for analyzing building sustainability metrics via Excel, PDF, and frameworks like ESG, LEED, BREEAM, DGNB with IFC integration.

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.

Autonomous, carbon-aware building management system that pairs EnergyPlus digital twins with LLMs via the Model Context Protocol (MCP) for dynamic HVAC optimization and grid carbon reduction.

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.

An unofficial read-only MCP server for Panasonic AiSEG2 home energy management controllers, enabling clients to read live power flow, per-circuit consumption, circuit names, daily totals, and historical energy/cost data from the device's local web interface.

Unofficial MCP server for the messdienst24.de utility portal, providing tools to retrieve heating and hot-water consumption data, download PDF reports, and compare usage trends.

An MCP server that lets an MCP client control IKEA TRÅDFRI smart plugs paired to a DIRIGERA hub via its local REST API.

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.