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MyAIServ: AI-Powered FastAPI Server with MCP 🚀

@eagurin

代码仓库

关于 MyAIServ: AI-Powered FastAPI Server with MCP 🚀

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

基本信息

分类

开发工具

许可证

MIT

运行时

python

传输方式

stdio

发布者

eagurin

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

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 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

FAQ from MyAIServ: AI-Powered FastAPI Server with MCP 🚀

What are the runtime dependencies?

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

How do I run tests?

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

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.

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).

What metrics are exposed?

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

评论

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