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AytchMCP - Aytch4K MCP Server

@aytch4k

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基本情報

カテゴリ

その他

ライセンス

GPL-3.0

トランスポート

stdio

作者

aytch4k

AytchMCP - Aytch4K MCP Server について

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 の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

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AytchMCP - Aytch4K MCP Server のツール

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この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

What LLM providers does AytchMCP support?

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

2

How is the server deployed?

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

3

How is configuration handled?

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

4

What components make up the server?

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

5

What package manager does it use?

It uses uv as the Python package manager.

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