Shared time-series domain layer and Plotly renderer for air-Q MCP providers
Project description
airq-mcp-timeseries
Shared time-series domain layer and Plotly renderer for air-Q MCP providers.
This project is not an MCP server. It is the shared package that consolidates
time-series querying, normalization, resampling, summarization and plotting so
that mcp-airq and mcp-airq-cloud can reuse the same business logic.
Features
- shared query, series, summary and plotting data models
- source-agnostic
TimeSeriesProviderprotocol - metric normalization and query validation
- resampling and peak-preserving downsampling
- renderer-independent plot model
- Plotly rendering for HTML, PNG, SVG and WebP
- CSV and Excel export for processed time-series data
- async high-level orchestration with
plot_history(),summarize_history()andexport_history()
Installation
pip install airq-mcp-timeseries
For development:
pip install -e ".[dev]"
pre-commit install
Usage
from datetime import datetime, timedelta
from airq_mcp_timeseries import HistoryQuery, PlotRequest, Selector, export_history, plot_history
request = PlotRequest(
selector=Selector(devices=["Living Room"]),
metric="co2",
start=datetime.now().astimezone() - timedelta(hours=6),
end=datetime.now().astimezone(),
)
result = await plot_history(provider, request)
export = await export_history(
provider,
HistoryQuery(
selector=request.selector,
metric=request.metric,
start=request.start,
end=request.end,
),
output_format="csv",
)
provider must implement the shared TimeSeriesProvider protocol.
Development
Run the full local validation stack:
pre-commit run --all-files
pytest --exitfirst -n auto
Release Process
- Update
versioninpyproject.toml. - Commit the change and create a matching Git tag such as
v0.1.0. - Publish a GitHub Release from that tag.
The publish workflow validates that the release tag matches
pyproject.toml, builds the package and publishes it to PyPI.
License
Apache License 2.0. See LICENSE.
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