"""Reference resource for resampling methods."""
from __future__ import annotations
from fastmcp import Context
from src.app import mcp
from src.shared.reference import list_resampling_methods, resampling_guide
@mcp.resource(
uri="reference://resampling/available/{category}",
name="Resampling Methods Available",
description=(
"Complete enumeration of available resampling methods from rasterio.enums.Resampling. "
"Lists all supported algorithms including nearest, bilinear, cubic, cubic_spline, lanczos, "
"average, mode, gauss, and max/min/med/q1/q3. Used by raster_reproject and raster_convert "
"with overviews. Filter by category: 'interpolation', 'aggregation', or 'all'."
),
)
def list_resampling_methods_resource(
category: str = "all",
ctx: Context | None = None,
) -> dict:
"""Return available resampling methods and usage guidance."""
normalized = None if not category or category.lower() == "all" else category
entries = list_resampling_methods(category=normalized)
if ctx and normalized:
ctx.debug(
f"Filtered resampling methods by category='{normalized}' -> {len(entries)} entries"
)
return {"entries": entries, "total": len(entries)}
@mcp.resource(
uri="reference://resampling/guide/{method}",
name="Resampling Selection Guide",
description=(
"Expert guidance for choosing appropriate resampling methods based on data type and "
"use case. Explains trade-offs between interpolation quality, performance, and data "
"integrity. Critical for raster_reproject operations per ADR-0011 requirement. "
"Covers: nearest (categorical/discrete data, preserves exact values), bilinear "
"(continuous data, smooth results), cubic (elevation/scientific, highest quality), "
"average (downsampling), mode (categorical downsampling). Filter by method name or 'all'."
),
)
def resampling_guide_resource(
method: str = "all",
ctx: Context | None = None,
) -> dict:
"""Return curated guide for choosing resampling strategies."""
normalized = None if not method or method.lower() == "all" else method
entries = resampling_guide(topic=normalized)
if ctx and normalized:
ctx.debug(f"Filtered resampling guide by method='{normalized}' -> {len(entries)} entries")
return {"entries": entries, "total": len(entries)}
