mcp-technical-analysis

This MCP server provides AI-powered cryptocurrency market analysis by exposing a broad set of market data, technical indicators, risk calculations, and trading capabilities via the Model Context Protocol. It combines 69 analysis and trading tools, 31 AI prompts, and 4 educational resources to support multi-timeframe market assessments, risk-aware decision making, and automated or semi-automated trading workflows. Typical usage involves connecting an AI assistant or decision-support agent to the MCP endpoint, then querying for real-time data (prices, indicators, volumes), performing multi-timeframe trend assessments, and invoking trading or risk-management tools as needed. The server also supports AI memory features to persist preferences and patterns across sessions for more personalized recommendations.

Prerequisites:

• Node.js (v14+ recommended) and npm/yarn installed on your machine
• Internet access to install the MCP server package via npx

Installation steps:

1. Ensure Node.js is installed:
   • macOS/Linux: curl -fsSL https://deb.nodesource.com/setup_18.x | bash - && sudo apt-get install -y nodejs
   • Windows: download and install from https://nodejs.org/
2. Verify npm installation:
   • npm -v
3. Run the MCP server via npx (this will pull the MCP package on first run):
   • npx -y mcp-technical-analysis
4. (Optional) Configure environment variables if required by the package (see docs):
   • export MCP_API_TOKEN=your_token_here
   • export MCP_LOG_LEVEL=info
5. Start the server and verify it’s listening on the expected port (default 8080 or as configured by the package).
6. Connect your MCP client or AI agent to the server endpoint to begin issuing MCP commands.

Note: If you prefer a persistent install, you can clone a repository or install via a package manager once the MCP package is published to npm and follow its specific startup instructions.

Tips and common considerations:

• API streaming: The server supports real-time updates via HTTP/SSE. If your client needs live data, ensure the connection is kept open and handle reconnection logic.
• 69 tools + 31 prompts: Explore the full suite of market data, order book insights, risk calculations, and AI prompts to tailor workflows to Day/ Swing/ Position trading.
• Hyperliquid integration: When using futures/spot tools, be mindful of testnet vs mainnet behavior and slippage settings.
• Memory features (Mem0): Use the persistent memory to store user preferences, trade journals, and pattern recognition results for improved recommendations over time.
• Environment variables: Some deployments may require API keys, endpoints, or feature flags. Review the provider's documentation for required and optional variables.
• Production readiness: The README indicates extensive capabilities; in a new environment, validate with small-scale tests before executing real trades.
• Troubleshooting: If the server fails to start, check Node.js version compatibility, port availability, and network egress rules. Review logs for missing dependencies or misconfigurations.