About ionis-mcp
ionis-mcp is an MCP server for amateur radio operators and researchers analyzing HF propagation with IONIS-AI datasets. Its tools cover cross-source observations, paths, bands, time periods, and solar conditions.
Key features
- 01Filter signatures by source, band, and grid
- 02Analyze paths and band openings
- 03Measure solar activity correlations
- 04Compare observation sources
- 05Query solar history and current conditions
Use cases
- 01Find opening times for a radio path
- 02Compare path data from WSPR and RBN
- 03Assess solar flux effects on propagation
MCP support
It exposes HF propagation analytics tools to MCP clients, including dataset queries, path analysis, solar correlation, and current conditions.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEionis-mcp
MCP server for HF radio propagation analytics on the IONIS-AI datasets — 175M+ aggregated signatures derived from 14 billion WSPR, RBN, contest, DXpedition and PSK Reporter observations, 2005–2026 — through any MCP-compatible AI assistant.
Part of the qso-graph project. No authentication required. The datasets are downloaded once (see Datasets).
Install
uv tool install ionis-mcp # puts ionis-mcp and ionis-download on your PATH
ionis-download --bundle minimal # 454 MB; see Datasets for the other bundles
Or with pip: pip install ionis-mcp.
Tools
| Tool | Description | Key Parameters |
|---|---|---|
list_datasets | Available datasets with row counts and file sizes | — |
query_signatures | Signature lookup filtered by source, band, grid, hour, month | source, band, tx_grid, rx_grid, hour, month |
band_openings | Hour-by-hour propagation profile for a path on one band | tx_grid, rx_grid, band |
path_analysis | A path across all bands, hours, months and sources | tx_grid, rx_grid, source |
solar_correlation | Solar flux effect on propagation, by SFI bracket | band, tx_grid, rx_grid |
grid_info | Maidenhead grid decode with solar elevation | grid, hour, month |
compare_sources | Cross-dataset comparison (WSPR vs RBN vs contest vs PSKR) | tx_grid, rx_grid, band |
dark_hour_analysis | Paths by solar geometry: both-day, cross-terminator, both-dark | band, hour, month |
solar_history | Historical solar indices for a date range | start_date, end_date, resolution |
band_summary | Band overview: hour distribution, top grid pairs, distances | band, source |
current_conditions | Live forecast: SFI, Kp, solar wind, band outlook, POTA/SOTA tips | qth_grid |
get_version_info | Service version + upstream spec version (fleet identity attestation) | — |
What is IONIS-AI?
IONIS-AI is an open-source machine learning system for predicting HF (shortwave) radio propagation. Its datasets are curated from the world's largest amateur radio telemetry networks and distributed as SQLite files on SourceForge. ionis-mcp lets an assistant answer propagation questions from them, no SQL required.
| Source | Signatures | Raw Observations | SNR Type | Years |
|---|---|---|---|---|
| WSPR | 93.6M | 10.9B beacon spots | Measured (-30 to +20 dB) | 2008-2026 |
| RBN | 67.3M | 2.3B CW/RTTY spots | Measured (8-29 dB) | 2009-2026 |
| CQ Contests | 5.7M | 234M SSB/RTTY QSOs | Anchored (+10/0 dB) | 2005-2025 |
| DXpeditions | 260K | 3.9M rare-grid paths | Measured | 2009-2025 |
| PSK Reporter | 8.4M | 514M+ FT8/WSPR spots | Measured (-34 to +38 dB) | Feb 2026+ |
| Solar Indices | — | 77K daily/3-hour records | SFI, SSN, Kp, Ap | 2000-2026 |
| DSCOVR L1 | — | 23K solar wind samples | Bz, speed, density | Feb 2026+ |
All signature tables share an identical 13-column schema (tx_grid, rx_grid, band, hour, month, median_snr, spot_count, snr_std, reliability, avg_sfi, avg_kp, avg_distance, avg_azimuth) — ready for cross-source analysis.
Datasets
# 1. Install
uv tool install ionis-mcp
# 2. Download datasets (to default location: ~/.ionis-mcp/data/)
ionis-download --bundle minimal # 454 MB — contest + solar + grids
ionis-download --bundle recommended # 1.1 GB — adds PSKR + DSCOVR
ionis-download --bundle full # 16.2 GB — all 9 datasets
# 3. Configure your MCP client (see Quick Start) and restart
That's it. Both ionis-download and ionis-mcp use the same default data directory. No environment variables needed.
Default data directory
| Platform | Location |
|---|---|
| Linux / macOS | ~/.ionis-mcp/data/ |
| Windows | %LOCALAPPDATA%\ionis-mcp\data\ |
Override with a custom path:
# Download to custom location
ionis-download --bundle minimal /path/to/my/data
# Tell the server where to find it
ionis-mcp --data-dir /path/to/my/data
# or
export IONIS_DATA_DIR=/path/to/my/data
Download individual datasets
# Pick specific datasets
ionis-download --datasets wspr,rbn,grids,solar
# See all available datasets and bundles
ionis-download --list
# Re-download (overwrite existing)
ionis-download --bundle minimal --force
If a download is interrupted (a dropped connection, Ctrl-C), run the same command again: it picks up where it stopped. Brief network errors are retried on their own. Sizes are in MB and GB (1 MB = 1,000,000 bytes).
Data directory layout
~/.ionis-mcp/data/ (or $IONIS_DATA_DIR)
├── propagation/
│ ├── wspr-signatures/wspr_signatures_v2.sqlite (9.1 GB, 93.6M rows)
│ ├── rbn-signatures/rbn_signatures.sqlite (6.0 GB, 67.3M rows)
│ ├── contest-signatures/contest_signatures.sqlite (445 MB, 5.7M rows)
│ ├── dxpedition-signatures/dxpedition_signatures.sqlite (22 MB, 260K rows)
│ └── pskr-signatures/pskr_signatures.sqlite (635 MB, 8.4M rows)
├── solar/
│ ├── solar-indices/solar_indices.sqlite (8.0 MB, 76.7K rows)
│ └── dscovr/dscovr_l1.sqlite (3.0 MB, 23K rows)
└── tools/
├── grid-lookup/grid_lookup.sqlite (1.1 MB, 31.7K rows)
└── balloon-callsigns/balloon_callsigns_v2.sqlite (0.1 MB, 1.5K rows)
The server works with whatever datasets are present. Missing datasets degrade gracefully — tools that need unavailable data return clear messages instead of errors.
Quick Start
Configure your MCP client
ionis-mcp works with any MCP-compatible client. Add the server config and restart. The tools appear automatically.
If you downloaded data to a custom location, add "env": { "IONIS_DATA_DIR": "/path/to/data" } to any config below.
Claude Desktop
Add to claude_desktop_config.json (~/Library/Application Support/Claude/ on macOS, %APPDATA%\Claude\ on Windows):
{
"mcpServers": {
"ionis": {
"command": "uvx",
"args": ["ionis-mcp"]
}
}
}
Claude Code
Add to .claude/settings.json:
{
"mcpServers": {
"ionis": {
"command": "uvx",
"args": ["ionis-mcp"]
}
}
}
ChatGPT Desktop
ChatGPT supports MCP via the OpenAI Agents SDK. Add under Settings > Apps & Connectors, or configure in your agent definition:
{
"mcpServers": {
"ionis": {
"command": "uvx",
"args": ["ionis-mcp"]
}
}
}
Cursor
Add to .cursor/mcp.json (project-level) or ~/.cursor/mcp.json (global):
{
"mcpServers": {
"ionis": {
"command": "uvx",
"args": ["ionis-mcp"]
}
}
}
VS Code / GitHub Copilot
Add to .vscode/mcp.json in your workspace:
{
"servers": {
"ionis": {
"command": "uvx",
"args": ["ionis-mcp"]
}
}
}
Gemini CLI
Add to ~/.gemini/settings.json (global) or .gemini/settings.json (project):
{
"mcpServers": {
"ionis": {
"command": "uvx",
"args": ["ionis-mcp"]
}
}
}
Installed with pip instead? Use "command": "ionis-mcp" in any config above.
Ask questions
"When is 20m open from Idaho to Europe?"
"How does solar flux affect 15m propagation?"
"Show me 10m paths at 03z where both stations are in the dark"
"Compare WSPR and RBN observations on 20m FN31 to JO51"
"What are the current band conditions? I'm heading out for POTA."
"What were the solar conditions during the February 2026 geomagnetic storm?"
Architecture
- Transport: stdio (Claude Desktop / Claude Code) or streamable-http (MCP Inspector)
- Database: Read-only
sqlite3connections (?mode=ro) — no writes, ever - Query safety: All queries use parameterized SQL (
?placeholders), result limits enforced server-side (max 1000 rows) - Grid lookup: 31.7K Maidenhead grids loaded into memory at startup (~2 MB) for instant lat/lon resolution
- Solar geometry: Pure Python solar elevation computation (same algorithm as the IONIS-AI training pipeline) — classifies endpoints as day/twilight/night for propagation context
- Cross-source queries: Each SQLite database opened separately, results merged in Python with source labels
MCP Inspector
ionis-mcp --transport streamable-http --port 8000
Then open the MCP Inspector at http://localhost:8000/mcp.
Development
git clone https://github.com/qso-graph/ionis-mcp.git
cd ionis-mcp
uv sync --group dev
uv run pytest
Related Projects
| Repository | Purpose |
|---|---|
| ionis-validate | IONIS-AI model validation suite (PyPI) |
| IONIS-AI datasets | Distributed dataset files (SourceForge) |
License
GPL-3.0-or-later
Citation
If you use the IONIS-AI datasets in research, please cite:
Beam, G. (KI7MT). IONIS: Ionospheric Neural Inference System — HF Propagation Prediction Datasets. SourceForge, 2026. https://sourceforge.net/projects/ionis-ai/
Comments
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