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AytchMCP - Aytch4K Model Context Protocol Server

@aytch4k

关于 AytchMCP - Aytch4K Model Context Protocol Server

Model Context Protocol (MCP) server implementation for Aytch4K applications. The MCP server provides an interface for Large Language Models (LLMs) to interact with Aytch4K applications.

基本信息

分类

其他

许可证

GPL-3.0

运行时

python

传输方式

stdio

发布者

aytch4k

配置

暂无标准配置

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代码仓库

工具

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概览

What is AytchMCP - Aytch4K Model Context Protocol Server?

AytchMCP is a Model Context Protocol server for Aytch4K applications, enabling Large Language Models to interact with application data and actions. It supports multiple LLM providers including OpenAI, Anthropic, OpenRouter.ai, and NinjaChat.ai.

How to use AytchMCP - Aytch4K Model Context Protocol Server?

The server is containerized using Docker; see the provided Dockerfiles and docker-compose.yml for setup. Configuration is managed through properties files for naming, variables, branding, and LLM integrations.

Key features of AytchMCP - Aytch4K Model Context Protocol Server

  • Uses fastmcp for MCP protocol compliance and routing
  • Exposes resources similar to GET endpoints
  • Provides tools with computation and side effects
  • Offers reusable prompt templates
  • Supports image data handling
  • Integrates OpenAI, Anthropic, OpenRouter.ai, and NinjaChat.ai

Use cases of AytchMCP - Aytch4K Model Context Protocol Server

  • Enabling LLMs to read data from Aytch4K applications via resources
  • Allowing LLMs to trigger actions in Aytch4K applications via tools
  • Providing structured prompts for consistent LLM interactions
  • Accessing a variety of LLM models through multiple providers
  • Containerized deployment for easy integration

FAQ from AytchMCP - Aytch4K Model Context Protocol Server

What LLM providers does AytchMCP support?

It supports OpenAI, Anthropic, OpenRouter.ai, and NinjaChat.ai.

How is the server deployed?

The MCP server is containerized using Docker with provided Dockerfiles and docker-compose.yml.

How is configuration handled?

Configuration is managed through properties files for customization of naming, variables, branding, and LLM integrations.

What components make up the server?

Components include fastmcp, resources, tools, prompts, images, and context.

What package manager does it use?

It uses uv as the Python package manager.

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