What LLM providers does AytchMCP support?
It supports OpenAI, Anthropic, OpenRouter.ai, and NinjaChat.ai.
AytchMCP - Aytch4K MCP Server
@aytch4k
基本信息
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 - Aytch4K MCP Server 配置
AytchMCP - Aytch4K MCP Server 工具
工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
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
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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