MyAIServ: AI-Powered FastAPI Server with MCP 🚀

@eagurin

About 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

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Frequently asked questions

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.

Comments

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