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.
Five new prompts guide LLMs through multi-tool workflows:
satellite_snapshot, climate_monitor, layer_deep_dive,
multi_layer_story, polar_watch. README documents all 11 tools,
5 resources, and 7 prompts with example conversations.
MIT license, project URLs, and updated classifiers for PyPI.
Address 20 findings from safety-critical review:
CRITICAL:
- C1: Replace ET.fromstring with defusedxml across all XML parsers
- C2: Fix client init failure leaving half-initialized state; clean
up HTTP client on startup failure so next connection can retry
HIGH:
- H1: Replace unbounded dicts with LRU caches (maxsize=500)
- H2: Move Nominatim rate limiter from module globals to per-instance
state on GIBSClient, eliminating shared mutable state
- H3: Validate _parse_rgb input, return (0,0,0) on malformed data
- H4: Add exponential backoff retry for capabilities loading
- H5: Invert WMS error detection to verify image content-type
- H6: Clamp image dimensions to 4096 max to prevent OOM
MEDIUM:
- M1: Convert images to RGB mode in compare_dates for RGBA safety
- M2: Narrow DescribeDomains XML matching to TimeDomain elements
- M3: Add BBox model_validator for coordinate range validation
- M4: Add ET.ParseError to colormap fetch exception handling
- M5: Replace bare except Exception with specific types in server
- M6: Catch ValueError from _resolve_bbox in imagery tools for
consistent error returns
- M7: Only cache successful geocoding lookups (no negative caching)
LOW:
- L3: Derive USER_AGENT version from package __version__
- L5: Remove unused start_date/end_date params from check_layer_dates
