Halex Launches "Weather Data MCP Server (Beta)" Capable of Linking with AI Agents

Halex Co., Ltd. (Headquarters: Tokyo, President: Hiroyuki Fujioka, hereinafter Halex), a private weather company, will begin offering the "Weather Data MCP Server (Beta)" from April 2026, which enables AI agents to refer to weather data using natural language. AI agents linked with this MCP server can automatically call various weather data APIs provided by Halex via the MCP server, based on natural language instructions from users. This allows users to instantly acquire and utilize necessary weather data for their operations without being conscious of API specifications or request generation. Furthermore, developers can incorporate weather data acquisition functions into their services with minimal implementation burden for API linkage, expecting improved development efficiency. --- ■ **Overview** As the corporate use of generative AI and AI agents progresses, there is a growing demand for mechanisms that allow AI to safely and reliably access external services and various data, and integrate them into business processes. MCP (Model Context Protocol) is a standard that standardizes such data access, enabling AI to acquire necessary information efficiently and uniformly. It is being adopted rapidly both domestically and internationally as a foundational technology to reduce the burden of development and integration. Halex began providing weather forecast data via API in 2012 and has supported companies in utilizing weather data for many years through expansion to historical weather data and GIS-oriented weather data. In collaboration with NTT DATA Group companies, we have promoted research and utilization of AI technology. This time, to make weather data reference from AI agents easier and further promote advanced data utilization by companies, we are launching the "Weather Data MCP Server" beta version as an option for various API services. This beta version is intended to be used for functional verification and requirement refinement tailored to actual business needs during the introduction and consideration phase of AI agents by companies. ■ **Expected Effects** 1. Improved convenience of weather data It eliminates the need for conventional API request implementation and format conversion, allowing weather data to be referenced solely through natural language. This minimizes development burden and enables speedy utilization. 2. Promotion of DX and weather data integration Company data (demand, inventory, processes, etc.) and weather data can be securely linked via MCP, enabling advanced decision support and automation based on context by LLMs. 3. Advanced trend analysis using historical weather data Halex's MCP server connects not only to weather forecast data but also to historical weather data API (HalexMemory!). AI agents can analyze trends by cross-referencing past weather conditions with various business results of customers (demand, sales, work processes, etc.). This refines the understanding of seasonality and weather-related patterns, supporting reproducible decision-making based on data, such as improving demand forecasting accuracy, early risk detection, and optimizing planning operations. ■ **Use Case of LLM × Weather Data MCP Server** When an LLM is instructed to analyze weather risks in long-distance delivery planning, the LLM sequentially acquires weather information for multiple points from the departure to the destination via the Weather Data MCP Server. It then visualizes weather risk factors by section and time zone in a dashboard format. Additionally, it presents the impact on driver working hours and recommended actions based on anticipated risks, supporting on-site decision-making.