What exchanges does my-mcp-server support?
It supports multiple exchanges via the CCXT library; specific exchange configurations can be added in `services/exchange_service.py`.
My MCP Server
@yanqinghao
基本情報
My MCP Server について
概要はまだありません
My 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": {
"my-mcp-server-yanqinghao": {
"command": "uv",
"args": [
"venv"
]
}
}
}My MCP Server のツール
5Analyze market trends and provide technical indicators
Get real-time or historical price data for specific cryptocurrencies
Generate AI-driven market insights and predictions
Analyze news sentiment and impact on specific coins
Generate summaries of recent crypto news
概要
What is my-mcp-server?
my-mcp-server is a high-performance cryptocurrency market data and analysis platform built on the Model Context Protocol (MCP). It integrates with multiple cryptocurrency exchanges via CCXT, stores time-series data in TimescaleDB, and aggregates crypto news from RSS feeds. It provides real-time market data, historical analysis, LLM-powered insights, and news sentiment analysis, enabling seamless integration with AI applications.
How to use my-mcp-server?
Install Python 3.9+, Docker Compose, and the uv package manager. Clone the repository, set up a virtual environment with uv venv and uv pip sync, start TimescaleDB via docker-compose up -d timescaledb, configure .env and config/settings.py, then run the server with uv run python main.py. Interact using any MCP client to call tools, read resources, or use prompts.
Key features of my-mcp-server
- Real-time market data from multiple cryptocurrency exchanges
- Historical price and volume data analysis with TimescaleDB
- LLM integration for market trend insights and predictions
- Crypto news aggregation with custom RSS feed subscriptions
- AI-powered news sentiment analysis
- MCP protocol support for easy integration with AI applications
Use cases of my-mcp-server
- Provide live crypto price feeds and historical data to AI trading agents
- Aggregate and analyze crypto news sentiment for market prediction
- Build multi-exchange portfolio tracking dashboards
- Backtest trading strategies using time-series historical data
- Power LLM-based investment research and trend reports
よくある質問
What are the runtime dependencies?
Python 3.9+, Docker Compose (for TimescaleDB), the `uv` package manager, and TimescaleDB. No external API keys are required for basic operation, but exchange API keys may be needed for private endpoints.
Where is historical data stored?
All time-series data is stored in a TimescaleDB instance (PostgreSQL extension) that must be running before the server starts. Data is organized into hypertables for efficient querying.
Can I add custom RSS feeds?
Yes. Edit the `RSS_FEEDS` list in `config/settings.py` to add or modify news sources. The system parses feeds with `feedparser` and supports custom parsing logic in `services/news_service.py`.
How does my-mcp-server communicate with clients?
It uses the Model Context Protocol (MCP) over HTTP/SSE by default. Clients connect as MCP applications to call tools, read resources, and invoke prompts. No authentication is mentioned in the README.
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