What LLM providers does AytchMCP support?
It supports OpenAI, Anthropic, OpenRouter.ai, and NinjaChat.ai.
AytchMCP - Aytch4K MCP Server
@aytch4k
Basic information
About 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 config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryAytchMCP - Aytch4K MCP Server tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
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
Frequently asked questions
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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