airq-mcp-timeseries 0.1.6

Shared time-series domain layer and Plotly renderer for air-Q MCP providers

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 TimeSeriesProvider protocol
• metric normalization and query validation
• resampling and peak-preserving downsampling
• renderer-independent plot model
• Plotly rendering for HTML plus matplotlib-based PNG, SVG and WebP exports
• CSV and Excel export for processed time-series data
• async high-level orchestration with plot_history(), summarize_history() and export_history()

Installation

pip install airq-mcp-timeseries

This package now includes Pillow as a runtime dependency so WebP rendering works out of the box.

For development:

uv sync --frozen --extra dev
uv run 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)

# PlotRequest defaults to PNG for token-efficient binary artifact responses.

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:

uv run pre-commit run --all-files
uv run ruff check .
uv run ruff format --check .
uv run pyright
uv run pytest --exitfirst -n auto

Release Process

1. Update version in pyproject.toml.
2. Commit the change and create a matching Git tag such as v0.1.2.
3. 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.