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Ticket Tailor API Integration

@shakermakerk

关于 Ticket Tailor API Integration

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基本信息

分类

开发工具

许可证

MIT license

运行时

python

传输方式

stdio

发布者

shakermakerk

配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块,请前往代码仓库查看安装说明。

代码仓库

工具

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概览

What is Ticket Tailor API Integration?

A Python MCP wrapper for the Ticket Tailor API that enables LLMs to interact with Ticket Tailor. It is aimed at developers building AI agents that need to retrieve events, orders, and perform actions on ticket data.

How to use Ticket Tailor API Integration?

Clone the repository, install dependencies, and create a .env file with your Ticket Tailor API key. Use the MCP server with Cursor by editing mcp.json or integrate it with your own agent frameworks (e.g., LangChain React agents). An example agent script is provided in example_langgraph_agent.py.

Key features of Ticket Tailor API Integration

  • Retrieve events with flexible filtering options
  • Fetch orders with comprehensive search parameters
  • Built on FastMCP for easy integration
  • Example agent included for LangChain React agents
  • Roadmap for implementing more API endpoints

Use cases of Ticket Tailor API Integration

  • Allow AI assistants to search and list events from Ticket Tailor
  • Enable chatbots to look up order information for customers
  • Build automated tools that manage ticket inventory and orders
  • Create AI-driven event management workflows

FAQ from Ticket Tailor API Integration

What are the installation requirements?

Python 3 and dependencies from requirements.txt. You also need a Ticket Tailor API key.

How do I configure the server?

Create a .env file in the root directory and add your Ticket Tailor API key based on the .env_example file.

Can I use this server with any LLM framework?

Yes, the server follows the MCP protocol. The README provides an example for LangChain React agents in example_langgraph_agent.py.

Does the server cover all Ticket Tailor API endpoints?

Not yet. Only retrieving events and orders are implemented. The project roadmap lists prioritized endpoints for future implementation.

How do I integrate with Cursor?

Edit mcp.json to add a command entry: "command": "mcp run /src/ticket_tailor/ticket_tailor_funcs.py" (path may need adjustment).

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