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MyAIServ: AI-Powered FastAPI Server with MCP Server

@eagurin

リポジトリ

基本情報

カテゴリ

開発者ツール

ライセンス

MIT

トランスポート

stdio

作者

eagurin

MyAIServ: AI-Powered FastAPI Server with MCP Server について

High-performance FastAPI server implementing Model Context Protocol (MCP) for seamless integration with Large Language Models (LLMs). Built with modern stack: FastAPI, Elasticsearch, Redis, Prometheus, and Grafana.

#myaiserv#fastapi#ai-server

MyAIServ: AI-Powered FastAPI Server with MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "myaiserv": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

MyAIServ: AI-Powered FastAPI Server with MCP Server のツール

ツール一覧はまだありません

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 MyAIServ: AI-Powered FastAPI Server with MCP 🚀?

MyAIServ is a FastAPI implementation of the Model Context Protocol (MCP), providing a standardized interface for interaction between LLM models and applications. It is intended for developers who need a high-performance, extensible API that bridges LLMs with tools, resources, and real-time communication.

How to use MyAIServ: AI-Powered FastAPI Server with MCP 🚀?

Clone the repository, install Poetry, then run poetry install to set up dependencies. Start the server with poetry run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload or via the just run command. The API is then accessible at http://localhost:8000, with Swagger UI at /docs, ReDoc at /redoc, and GraphQL Playground at /graphql.

Key features of MyAIServ: AI-Powered FastAPI Server with MCP 🚀

  • High-performance API built on FastAPI with async operations
  • Full MCP support for resources, tools, prompts, and sampling
  • Prometheus/Grafana monitoring with metrics at /metrics
  • Extensible via simple interfaces for new tools
  • GraphQL API for flexible data queries
  • WebSocket support for real-time interactions
  • Semantic search integration with Elasticsearch
  • Redis caching for improved performance

Use cases of MyAIServ: AI-Powered FastAPI Server with MCP 🚀

  • Providing LLMs with standardized access to tools like file operations, weather, text analysis, and image processing
  • Real-time data exchange between applications and LLMs via WebSocket
  • Flexible data querying and mutation using GraphQL
  • Monitoring API usage and performance metrics in production

よくある質問

1

What are the runtime dependencies?

Python 3.9+ and Poetry are required. Redis and Elasticsearch are optional but recommended for caching and semantic search.

2

How do I run tests?

Tests can be run using `poetry run pytest` or `just test`.

3

Does the server support Docker?

Yes, the project includes a `docker-compose.yml` for containerized deployment. You can run all services with `docker compose up -d`.

4

How can I integrate this with an LLM?

Retrieve available tools via GET `/tools`, then include those tools in your LLM API request (e.g., using `tools` and `tool_choice: "auto"` in the chat completion call).

5

What metrics are exposed?

Prometheus metrics are available at `/metrics`, including request counts per tool, execution times, and error counts.

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