DDR-Price-MCP

# RAM Prices MCP Server Lightweight Model Context Protocol (MCP) server that caches and serves weekly DDR/DRAM price series from [memorys.com](https://en.memorys.com/price/ews/), plus historical memory technology data from [hblok.net](https://hblok.net/storage_data/memory.csv). It scrapes per-quarter pages, stores history in SQLite, and exposes tools for refresh, validation, analytics, forecasting, and chart rendering. ## Features - Discovers available quarters and caches price history in SQLite (no re-download of old data unless forced). - Tools for refresh, integrity checks, validation, resampling, returns, rolling stats, regime detection, forecasting (naive/ETS/ARIMA/trend), and PNG chart rendering. - Forecasts fall back to naive when `statsmodels` is unavailable. - Memory.csv tooling for filtering by memory type and exploring historical costs. - Exposes results in CSV/JSONL for downstream analysis. ## Requirements - Python 3.10+ recommended. - Dependencies: see `requirements.txt` (`mcp`, `requests`, `matplotlib`; `statsmodels` optional but recommended). ## Installation ```bash python -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` ## Configuration - `RAM_MCP_DB_PATH`: path to SQLite cache (default `~/.cache/ram-mcp/prices.sqlite`). - `RAM_MCP_DEFAULT_PRODUCT_ID`: default memorys.com product ID (default `100227`). - TTLs: `RAM_MCP_BASEPAGE_TTL_HOURS`, `RAM_MCP_CURQ_TTL_HOURS`, `RAM_MCP_OLDQ_TTL_DAYS`. - `MCP_TRANSPORT`: `stdio` (default) or `streamable-http` for HTTP transport. ## Running the server ```bash python ram_prices_mcp_server.py # MCP_TRANSPORT=stdio # or MCP_TRANSPORT=streamable-http python ram_prices_mcp_server.py # exposes an HTTP MCP endpoint ``` Server logs go to stderr; do not rely on stdout in MCP clients. ## Module layout - `ram_prices_mcp_server.py`: MCP tool definitions and entrypoint only. - `ram_mcp/config.py`: configuration/constants and defaults. - `ram_mcp/storage.py`: SQLite cache and persistence. - `ram_mcp/http_client.py`: HTTP session/retry logic. - `ram_mcp/memorys.py`: memorys.com refresh/parsing. - `ram_mcp/memory_csv.py`: memory.csv ingestion and cache. - `ram_mcp/series.py`: time-series transforms/analytics. - `ram_mcp/forecast.py`: ETS/ARIMA/trend forecasting. - `ram_mcp/charts.py`: PNG chart rendering. - `ram_mcp/validation.py`: input validation helpers. ## Key MCP tools - `ram_refresh` / `ram_refresh_range`: refresh cache (optionally force re-download). - `ram_cache_status`, `ram_integrity_check`, `ram_verify_sorting`, `ram_validate`: health checks for coverage, ordering, and data quality. - `ram_get_history`, `ram_get_series`, `ram_returns`, `ram_growth`, `ram_rolling_stats`, `ram_regime_detect`: analysis helpers. - `ram_forecast`, `ram_forecast_compare`: cached forecasts (naive/ETS/ARIMA/trend). - `ram_export`: export cached points as CSV/JSONL. - `ram_memory_types`, `ram_memory_history`: explore memory.csv by type/year. - `ram_render_price_chart`, `ram_render_memory_chart`: PNG chart rendering (supports optional forecast overlay). ## Example workflows ```bash # Refresh default product (respect TTLs) python ram_prices_mcp_server.py # start the server, then from client call: ram_refresh # Export cached data ram_export export_format=csv > ddr_prices.csv ``` ## Client configuration examples (`mcp.json`) LM Studio: ```json { "servers": { "ram-prices": { "transport": { "type": "stdio", "command": "python", "args": ["ram_prices_mcp_server.py"] } } } } ``` Claude Desktop (macOS/Windows) — same stdio transport: ```json { "servers": { "ram-prices": { "transport": { "type": "stdio", "command": "python", "args": ["ram_prices_mcp_server.py"], "env": { "RAM_MCP_DB_PATH": "C:/Users/you/.cache/ram-mcp/prices.sqlite" } } } } } ``` HTTP client / proxy (MCP over HTTP): ```json { "servers": { "ram-prices-http": { "transport": { "type": "http", "url": "http://127.0.0.1:8000" }, "command": "python", "args": ["ram_prices_mcp_server.py"], "env": { "MCP_TRANSPORT": "streamable-http" } } } } ``` Generic `mcpServers` layout (alternate clients): ```json { "mcpServers": { "ram-prices": { "command": "python", "args": ["/path/to/project/ram_prices_mcp_server.py"], "env": { "RAM_MCP_DEFAULT_PRODUCT_ID": "100227", "RAM_MCP_DB_PATH": "/path/to/project/prices.sqlite", "RAM_MCP_BASEPAGE_TTL_HOURS": "12", "RAM_MCP_CURQ_TTL_HOURS": "6" } } } } ``` ## Testing & linting - Run unit tests: `pytest`. - Use `ram_validate`, `ram_verify_sorting`, and `ram_integrity_check` after refreshes to sanity-check data. - Suggested linting: `ruff` or `flake8`; aim for zero warnings. ## Data sources - memorys.com price series: https://en.memorys.com/price/ews/ - Memory technology history CSV: https://hblok.net/storage_data/memory.csv ## Contributing - See `AGENTS.md` for contributor guidelines (structure, style, PR expectations). - Keep the user agent stable and honor TTLs to avoid hammering [memorys.com](https://en.memorys.com/price/ews/).