Tongchenglvxing MCP Server

The Tongchenglvxing MCP Server is an unofficial bridge that lets AI assistants tap into the travel services of 同程旅行—specifically, its train ticketing API. By exposing a lightweight set of tools over the Model Context Protocol (MCP), developers can add real‑world travel data to conversational agents without having to build custom scrapers or maintain API keys. The server currently focuses on two core capabilities: retrieving a list of train schedules for a specified route and reporting the current server time, both delivered as MCP‑compatible tool calls.

What Problem Does It Solve?

Travel planning often requires up‑to‑date ticket availability and timing information. Traditional approaches involve embedding web requests directly into the assistant’s code or using third‑party travel APIs that may have restrictive rate limits. The Tongchenglvxing MCP Server abstracts these details behind a standardized protocol, allowing an AI assistant to request train schedules or the current time as if it were calling a local function. This reduces integration friction, keeps credentials out of the assistant’s runtime, and ensures that data remains fresh through the server’s own caching or request‑throttling logic.

Core Features and Value

• Train Schedule Query – Given a departure city, destination, and date, the server returns a structured list of available train numbers, departure/arrival times, and seat availability.
• Current Time Retrieval – A lightweight tool that returns the server’s UTC timestamp, useful for time‑sensitive prompts or logging.
• MCP Compatibility – Implements the MCP Typescript SDK, enabling seamless discovery of resources, prompts, and sampling options by any MCP‑enabled client.
• Node.js/TypeScript Friendly – Designed for rapid deployment in JavaScript environments, with future plans to support Java clients.
• User‑Agent Handling – Optimized request headers reduce the risk of being blocked by the travel site’s anti‑scraping measures.

Use Cases

• Travel Chatbots – A customer support bot can ask a user for travel dates and immediately provide train options without leaving the conversation.
• Personal Assistants – Voice‑activated assistants can answer “What trains are available from Shanghai to Beijing tomorrow?” by invoking the MCP tool.
• Data‑Driven Analytics – Applications that aggregate travel data can fetch schedule information on demand, feeding into dashboards or recommendation engines.
• Time‑Sensitive Operations – The time tool allows agents to confirm deadlines or synchronize actions across distributed services.

Integration with AI Workflows

Developers add the server to their MCP client configuration, after which the assistant automatically discovers the two tool methods. When a user query matches the intent of “train schedule” or “current time,” the assistant calls the appropriate tool, receives a JSON payload, and can incorporate it into its response. Because the server is stateless and statically typed through the MCP SDK, developers can confidently extend or replace endpoints without breaking existing contracts.

Unique Advantages

• No API Key Needed – By scraping the public 同程旅行 site through a controlled server, users avoid managing third‑party credentials.
• Open Source & Extensible – The codebase is available on GitHub, allowing contributors to add new travel services (e.g., flights or hotels) with minimal effort.
• Cross‑Platform – While currently TypeScript‑centric, the architecture is designed for future Java support, broadening adoption across backend stacks.

In summary, the Tongchenglvxing MCP Server empowers AI assistants to deliver accurate, real‑time train travel information with minimal integration overhead, making it an essential tool for developers building travel‑focused conversational experiences.