Ryan Malloy
ca7bc265f0
Fetch script uses FastMCP Client to call tools through MCP protocol. Includes Amazon true color, global temperature with legend, and Arctic sea ice March vs September comparison. Conversational examples now grounded in actual tool output.
111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
"""Fetch example images for the README via the mcgibs MCP server.
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Uses the FastMCP in-process Client to call tools through the MCP protocol,
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exactly as any MCP client would. Images are saved to docs/images/.
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"""
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import asyncio
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import base64
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import sys
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from pathlib import Path
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from fastmcp import Client
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# Wire up the server module so the client is initialized
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import mcgibs.server as server_module
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from mcgibs.client import GIBSClient
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from mcgibs.server import mcp
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OUT = Path(__file__).parent.parent / "docs" / "images"
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async def save_imagery(client: Client, filename: str, tool: str, params: dict) -> None:
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"""Call an imagery tool and save the returned image to disk."""
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print(f" Fetching {filename}...", file=sys.stderr)
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result = await client.call_tool(tool, params)
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# Find the ImageContent in the response
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for content in result.content:
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if content.type == "image":
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img_bytes = base64.b64decode(content.data)
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path = OUT / filename
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path.write_bytes(img_bytes)
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size_kb = len(img_bytes) / 1024
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print(f" Saved {path} ({size_kb:.0f} KB)", file=sys.stderr)
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return
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# If we got text only (error case), print it
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for content in result.content:
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if content.type == "text":
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print(f" Warning: {content.text[:200]}", file=sys.stderr)
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async def main():
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OUT.mkdir(parents=True, exist_ok=True)
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# Initialize GIBS client (normally done by middleware on first connection)
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print("Initializing GIBS client...", file=sys.stderr)
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gibs = GIBSClient()
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await gibs.initialize()
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server_module._client = gibs
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print(f"Ready — {len(gibs.layer_index)} layers loaded", file=sys.stderr)
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try:
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async with Client(mcp) as client:
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# 1. Amazon true color — natural satellite view
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await save_imagery(client, "amazon-true-color.jpg", "get_imagery", {
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"layer_id": "MODIS_Terra_CorrectedReflectance_TrueColor",
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"date": "2025-08-01",
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"place": "Amazon Rainforest",
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"width": 1024,
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"height": 768,
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"format": "jpeg",
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})
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# 2. Global surface air temperature — scientific data product
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await save_imagery(client, "temperature-global.jpg", "get_imagery", {
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"layer_id": "AIRS_L3_Surface_Air_Temperature_Daily_Day",
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"date": "2025-07-15",
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"bbox": [-180, -90, 180, 90],
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"width": 1024,
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"height": 512,
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"format": "jpeg",
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})
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# 3. Arctic sea ice comparison — March (max) vs September (min)
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await save_imagery(
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client, "arctic-ice-comparison.jpg", "compare_dates", {
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"layer_id": "AMSRU2_Sea_Ice_Concentration_12km",
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"date_before": "2025-03-01",
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"date_after": "2025-09-01",
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"bbox": [-180, 60, 180, 90],
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},
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)
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# 4. Surface temperature colormap legend
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await save_imagery(
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client, "temperature-legend.png", "get_legend", {
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"layer_id": "AIRS_L3_Surface_Air_Temperature_Daily_Day",
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"orientation": "horizontal",
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},
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)
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# 5. Colormap explanation (text, not image)
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print(" Fetching temperature colormap explanation...", file=sys.stderr)
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result = await client.call_tool(
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"explain_layer_colormap",
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{"layer_id": "AIRS_L3_Surface_Air_Temperature_Daily_Day"},
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)
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explanation = result.content[0].text
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(OUT / "temperature-colormap.txt").write_text(explanation)
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print(f" Saved colormap explanation", file=sys.stderr)
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finally:
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await gibs.close()
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server_module._client = None
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print("Done!", file=sys.stderr)
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asyncio.run(main())
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