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wspr-ai-lite Documentation

Lightweight WSPR analytics and AI‑ready backend using DuckDB + Streamlit, with data-safe query access via MCP Agents.

Resources

MCP Made with Streamlit DuckDB Docs License: MIT

Overview

wspr-ai-lite is a lightweight analytics tool for Weak Signal Propagation (WSPR) data. It combines DuckDB for local storage, Streamlit for visualization, and is now AI-Agent ready via MCP.

  • MCP Integration: Experimental MCP server (wspr-ai-lite-mcp) exposing safe APIs for AI agents. A manifest defines permitted queries and access control.
  • Analytics Dashboard: Streamlit UI lets you explore WSPR spots with SNR trends, DX distance analysis, station activity, and “QSO‑like” reciprocity views.
  • Canonical Schema: Data is normalized into a portable DuckDB file—consistent, lightweight, and ready for future backend upgrades.
  • CLI Tools: Click-based tools (wspr-ai-lite, wspr-ai-lite-fetch, wspr-ai-lite-tools) for downloading, ingesting, verifying, and managing the database.
  • Roadmap (v0.4+ vision): MCP server will migrate to a FastAPI + Uvicorn backend with service control (start/stop/restart), enabling production-grade deployment.

Technology Stack Key Benefits

MCP & AI Agents — safe, structured access for AI assistants. - Controlled: manifest defines exactly what tools/queries are exposed. - Interoperable: model-agnostic, works across many LLMs. - Extendable: add analytics or summary tools without altering the DB/UI. - Future-ready: aligns with open standards for AI-assisted research.

DuckDB — an embedded, columnar SQL database optimized for analytics. - High performance: in-memory processing, vectorized execution, columnar storage. - Lightweight: no external server needed, works anywhere Python runs. - Flexible: reads/writes CSV, Parquet, JSON; integrates directly with Pandas.

Streamlit — a Python-first framework for interactive data apps. - Rapid prototyping: build dashboards with just Python. - Interactive: real-time widgets, dynamic filters, custom layouts. - Visualization: integrates with Matplotlib, Plotly, Altair, and more.

Quick Workflow

  1. Fetch + Ingest Data → Download WSPRNet monthly archives, normalize into DuckDB.
  2. Explore in UI → Interactive dashboard with SNR, trends, distance/DX, activity heatmaps.
  3. Optional: MCP Tools → Query WSPR data safely from AI agents.

Documentation Index

  • Ingest Data — Fetch and normalize WSPRNet archives into DuckDB.
  • UI Guide — Launch and navigate the Streamlit dashboard.
  • Developer Setup — Get started contributing to wspr-ai-lite.

Further Reading