MCP services are completely internal to the container and accessed
only via localhost. They should not be configurable or exposed.
Changes:
- Remove MATH_HTTP_PORT, SEARCH_HTTP_PORT, TRADE_HTTP_PORT,
GETPRICE_HTTP_PORT from docker-compose.yml environment
- Remove MCP service port mappings from docker-compose.yml
- Remove MCP port configuration from .env.example
- Update README.md to remove MCP port configuration
- Update CLAUDE.md to clarify MCP services use fixed internal ports
- Update CHANGELOG.md with these simplifications
Technical details:
- MCP services hardcode to ports 8000-8003 via os.getenv() defaults
- Services only accessed via localhost URLs within container:
- http://localhost:8000/mcp (math)
- http://localhost:8001/mcp (search)
- http://localhost:8002/mcp (trade)
- http://localhost:8003/mcp (price)
- No external access needed or desired for these services
- Only API (8080) and web dashboard (8888) should be exposed
Benefits:
- Simpler configuration (4 fewer environment variables)
- Reduced attack surface (4 fewer exposed ports)
- Clearer architecture (internal vs external services)
- Prevents accidental misconfiguration of internal services
Simplifies deployment configuration by removing the RUNTIME_ENV_PATH
environment variable, which is no longer needed for API mode.
Changes:
- Remove RUNTIME_ENV_PATH from docker-compose.yml
- Remove RUNTIME_ENV_PATH from .env.example
- Update CLAUDE.md to reflect API-managed runtime configs
- Update README.md to remove RUNTIME_ENV_PATH from config examples
- Update CHANGELOG.md with this simplification
Technical details:
- API mode dynamically creates isolated runtime config files via
RuntimeConfigManager (data/runtime_env_{job_id}_{model}_{date}.json)
- tools/general_tools.py already handles missing RUNTIME_ENV_PATH
gracefully, returning empty dict and warning on writes
- No functional impact - all tests pass without this variable set
- Reduces configuration complexity for new deployments
Breaking change: None - variable was vestigial from batch mode era
Provides comprehensive guidance for working with the AI-Trader codebase including:
- Development commands for setup, data preparation, and running simulations
- Architecture overview of agent system, MCP toolchain, and data flow
- Configuration system with multi-layered priority
- Data formats for positions and price data
- Implementation details and common troubleshooting steps
