Enables vector search capabilities for AI queries, allowing efficient similarity searches and semantic retrieval of data stored in Elasticsearch indices.
Powers the REST, GraphQL, and WebSocket API interfaces, enabling different methods of interacting with the AI models through standardized endpoints.
Visualizes AI system metrics and performance data, providing dashboards for monitoring model behavior and operational health.
Offers a flexible query language interface for AI interactions, allowing clients to request exactly the data they need from the AI system.
Collects metrics from AI model operations, enabling detailed monitoring of performance, usage patterns, and resource utilization.
Provides caching and real-time data handling for AI operations, enhancing response times and supporting state management across requests.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MyAIServ MCP Server analyze the sentiment of this customer review"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Server is a FastAPI-based implementation of the Model Context Protocol (MCP) that provides a standardized interface for interaction between LLM models and applications.
🚀 High-performance API based on FastAPI and asynchronous operations
🔄 Full MCP support with resources, instruments, prompts and sampling
📊 Monitoring and metrics via Prometheus and Grafana
🧩 Extensibility through simple interfaces to add new tools
📝 GraphQL API for flexible work with data
💬 WebSocket support for real-time interaction
🔍 Semantic search via integration with Elasticsearch
🗃️ Caching via Redis for improved performance
📦 Manage dependencies via Poetry for reliable package management
Related MCP server: MCP Platform
Clone repository:
git clone https://github.com/yourusername/myaiserv.git
cd myaiservInstall Poetry (if not already installed):
curl -sSL https://install.python-poetry.org | python3 -Install dependencies via Poetry:
poetry installpoetry run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reloadOr via the just utility:
just runAfter launch, the API is available at: http://localhost:8000
Swagger UI: http://localhost:8000/docs
ReDoc: http://localhost:8000/redoc
GraphQL Playground: http://localhost:8000/graphql
myaiserv/
├── app/
│ ├── core/ # Базовые компоненты MCP
│ │ ├── base_mcp.py # Абстрактные классы MCP
│ │ └── base_sampling.py # Базовые классы для сэмплирования
│ ├── models/ # Pydantic модели
│ │ ├── mcp.py # Модели данных MCP
│ │ └── graphql.py # GraphQL схема
│ ├── services/ # Бизнес-логика
│ │ └── mcp_service.py # Сервис MCP
│ ├── storage/ # Хранилище данных
│ ├── tools/ # Инструменты MCP
│ │ ├── example_tool.py # Примеры инструментов
│ │ └── text_processor.py # Инструмент обработки текста
│ ├── utils/ # Утилиты
│ └── main.py # Точка входа FastAPI
├── app/tests/ # Тесты
├── docs/ # Документация
│ └── MCP_API.md # Описание API
├── pyproject.toml # Конфигурация Poetry и инструментов
└── .justfile # Задачи для утилиты justA file system tool that supports reading, writing, deleting and listing files.
curl -X POST "http://localhost:8000/tools/file_operations" \
-H "Content-Type: application/json" \
-d '{"operation": "list", "path": "."}'A tool for obtaining weather data by coordinates.
curl -X POST "http://localhost:8000/tools/weather" \
-H "Content-Type: application/json" \
-d '{"latitude": 37.7749, "longitude": -122.4194}'A tool for text analysis, including sentiment detection and summarization.
curl -X POST "http://localhost:8000/tools/text_analysis" \
-H "Content-Type: application/json" \
-d '{"text": "Example text for analysis", "analysis_type": "sentiment"}'A tool for text processing, including formatting, statistics calculation, entity extraction.
curl -X POST "http://localhost:8000/tools/text_processor" \
-H "Content-Type: application/json" \
-d '{"operation": "statistics", "text": "Example text", "stat_options": ["chars", "words"]}'An image processing tool that supports resizing, cropping and applying filters.
curl -X POST "http://localhost:8000/tools/image_processing" \
-H "Content-Type: application/json" \
-d '{"operation": "resize", "image_data": "base64...", "params": {"width": 800, "height": 600}}'To connect to the WebSocket API:
const socket = new WebSocket("ws://localhost:8000/ws");
socket.onopen = () => {
socket.send(JSON.stringify({
type: "initialize",
id: "my-request-id"
}));
};
socket.onmessage = (event) => {
const data = JSON.parse(event.data);
console.log("Received:", data);
};Examples of queries via GraphQL:
# Получение списка всех инструментов
query {
getTools {
name
description
}
}
# Выполнение инструмента
mutation {
executeTool(input: {
name: "text_processor",
parameters: {
operation: "statistics",
text: "Example text for analysis"
}
}) {
content {
type
text
}
is_error
}
}To run tests, use Poetry:
poetry run pytestOr via the just utility:
just testdocker compose up -dTo launch individual services:
docker compose up -d web redis elasticsearchMCP Server provides a standardized interface for integration with LLM models from various vendors:
import httpx
async def query_mcp_with_llm(prompt: str):
async with httpx.AsyncClient() as client:
# Запрос к MCP для получения контекста и инструментов
tools_response = await client.get("http://localhost:8000/tools")
tools = tools_response.json()["tools"]
# Отправка запроса к LLM с включением MCP контекста
llm_response = await client.post(
"https://api.example-llm.com/v1/chat",
json={
"messages": [
{"role": "system", "content": "You have access to the following tools:"},
{"role": "user", "content": prompt}
],
"tools": tools,
"tool_choice": "auto"
}
)
return llm_response.json()MCP Server provides metrics in Prometheus format via the /metrics endpoint. Metrics include:
Number of requests to each tool
Query execution time
Errors and exceptions
To format code and check it with linters:
just fmt
just lintThis server cannot be deployed
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