Vilnius Transport MCP Server is an implementation of the Model Context Protocol (MCP) that provides access to Vilnius public transport data for Large Language Models (LLMs). It enables LLMs to answer queries related to public transport stops and routes by exposing tools like 'find_stops' and 'find_closest_stop'.
This server extends the capabilities of LLMs by integrating real-time transport data using the MCP standard. It allows models to securely interact with external tools, access real-time or local data, call external functions, and maintain consistent tool interfaces.
The project was developed by sarunasdaujotis and is available on GitHub under the repository name 'sarunasdaujotis/vilnius-transport-mcp-server'.
The server can be integrated into development environments like Claude to provide LLMs with access to Vilnius public transport data. It requires configuration in the claude_desktop_config.json file and proper setup of the local directory path.
The initial commit for the project was made during its creation, though the exact date isn't provided in the given information.
To run the server, you need to execute the command: `uv run client.py path/src/vilnius_transport_mcp/transport.py`. Ensure the directory path matches your local installation.
The server exposes two main tools: 'find_stops', which searches for public transport stops by name, and 'find_closest_stop', which finds the closest public transport stop based on given coordinates.
The Model Context Protocol (MCP) is a standard that allows Large Language Models (LLMs) to securely access external tools and data, enabling them to interact with real-time or local data, call external functions, and maintain consistent tool interfaces.
Yes, you need to adjust the directory path in the `claude_desktop_config.json` file to match your local installation when adding the MCP server to your environment.
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.
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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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