# MCP Climate Example Repo

This repository contains a small, deterministic example that can be called through an MCP.

## Mock scientific purpose of the example

This is an example that uses mock climate data and aggregates and plots the data. Input: `data/mock_climate.csv` with `date, temperature_c, precipitation_mm, humidity_pct` values per day for one month. 
The output is (1) `outputs/climate_summary.csv` with `month, avg_temperature_c, total_precipitation_mm` for each month. The daily temperature values are aggregated using their mean, while the daily precipitation values are aggregated using their sum; (2) a plot of the data over time.

## Mock user workflow

The user workflow is the following:
1. User obtains `data` from (server, weather station)
2. User prepares the `config` file for the run
3. User executes the script using `data` and `config`
4. User verifies the data by visual inspection of the plot and the summary

This workflow currently requires the user to install the package (hurdle 1), write in a yaml file (hurdle 2), and provide relative paths (hurdle 3). It also requires the user to be in the correct relative directory when running the script (hurdle 4).

## Relevant repo content

- `config/example.yaml`: input parameters for the processing run
- `data/mock_climate.csv`: mock climate time-series data
- `scripts/process_climate.py`: processing and plotting script
- `outputs/`: generated summary CSV and plot files
- `tests/`: unit tests for the processing/plotting script and the MCP server

## Data processing order in the script

The script does the following in the given order:
1. Parses the command-line arguments giving the relative position of the config file: `parse_args()` 
2. Reads the YAML config file: `load_config()`
3. Loads the mock climate CSV: `run_pipeline()` -> `pd.read_csv()`
4. Cleans and converts the climate data: `prepare_data()`
5. Aggregates the daily data to monthly and saves to `outputs/`: `build_monthly_summary()`
6. Creates and exports a plot of the data: `create_plot()`

## Missing data policy:

- The config takes an optional top-level missing_policy block.
- The four values and what each does: interpolate / zero_fill / drop / fail.
- Defaults: interpolate for temperature_c, zero_fill for precipitation_mm

## Installing and executing the script

The necessary dependencies can be installed into a Python environment using `pip` or `uv`:
```bash
pip install -r requirements.txt
```

The script is then executed using 
```bash
python scripts/process_climate.py --config config/example.yaml
```

## Expected outputs

The expected outputs are stored in the folder given in the config file, for the default values:
- `outputs/climate_summary.csv`
- `outputs/climate_plot.png`

## Unit tests

Unit tests require installing dev dependencies and can then be run via
```bash
pip install -r requirements-dev.txt
python -m pytest
```