AytchMCP - Aytch4K Model Context Protocol Server
LocalAbout AytchMCP - Aytch4K Model Context Protocol Server
AytchMCP is an MCP server for connecting LLMs with Aytch4K applications. It exposes resources, tools, prompts, and image handling, with configurable settings for reuse across clients.
Key features
- 01Resources for LLM access
- 02Tools for application actions
- 03Reusable interaction prompts
- 04Image data handling
- 05Configurable properties files
Use cases
- 01Let LLMs access Aytch4K data
- 02Trigger actions in Aytch4K apps
MCP support
The MCP server exposes Aytch4K resources, tools, and prompts to MCP clients, and handles protocol connections and message routing.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEAytchMCP - Aytch4K Model Context Protocol Server
This directory contains the 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.
Components
- uv: Python package manager for managing Python projects
- fastmcp: Core interface to the MCP protocol, handling connection management, protocol compliance, and message routing
- resources: Data sources exposed to LLMs (similar to GET endpoints in a REST API)
- tools: Components that allow LLMs to take actions through the server (with computation and side effects)
- prompts: Reusable templates for LLM interaction
- images: Handling of image data
- context: Access to MCP capabilities for tools and resources
LLM Integrations
AytchMCP supports multiple LLM providers:
- OpenAI: GPT-4, GPT-3.5, and other OpenAI models
- Anthropic: Claude and other Anthropic models
- OpenRouter.ai: Access to a wide variety of models through a single API
- NinjaChat.ai: Access to NinjaChat's models
Docker Setup
The MCP server is containerized using Docker:
Dockerfile.build: For building the MCP serverDockerfile.run: For running the MCP serverdocker-compose.yml: For orchestrating the MCP services
Configuration
Configuration is managed through properties files to allow for customization of:
- Naming
- Variables
- Branding
- LLM integrations (OpenAI, Anthropic, OpenRouter.ai, NinjaChat.ai)
- Other service integrations
This approach ensures the MCP server can be reused across different clients with minimal changes.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.