AytchMCP - Aytch4K MCP Server

@aytch4k

代码仓库

基本信息

分类

其他

许可证

GPL-3.0

传输方式

stdio

作者

aytch4k

关于 AytchMCP - Aytch4K MCP 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.

#aytchmcp#model-context-protocol #llm-integration

AytchMCP - Aytch4K MCP Server 配置

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代码仓库

AytchMCP - Aytch4K MCP Server 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

常见问题

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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