mcp-tenki is a specialized MCP server that provides weather information for Japan. It is built using Python and can be run either by cloning the repository or directly via a remote Git source.
mcp-tenki was developed to serve as a dedicated server for providing Japanese weather data, likely for integration into applications or services requiring localized meteorological information.
The project has been developed by two main contributors: Y.Hashimoto (@gsy0911) and Kaito Oka (@Kit-Ok).
The source code for mcp-tenki is hosted on GitHub under the repository acxelerator/mcp-tenki. It can be cloned or run directly via its Git URL.
The exact last commit date is not provided in the available content, but recent updates include changes such as argument format specification for AI compatibility and folder renaming.
To run the server without cloning, use the 'uvx' command with arguments pointing to the Git repository. To run it after cloning, use the 'uv' command with the directory path of the cloned repository and specify the main.py script.
mcp-tenki requires Python and uses 'uv' for dependency management and execution. Ensure you have these installed before setting up the server.
The repository includes a README.md file, which likely contains setup instructions and other relevant details about the project.
Yes, as an MCP server, mcp-tenki can potentially integrate with systems that support modular server components, provided they align with its functionality.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
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