Discover pre-event and post-event imagery and compare it with archived flood extents. Compare listed sources, access requirements and evidence for satellite flood extent mcp.
4 sources · For ecologists, GIS analysts, hydrologists, flood consultants, drainage engineers, environmental consultants using AI-assisted workflows, geotechnical engineers. Enrichment describes potential applications; review source evidence and limitations.
Observed surface water is not automatically floodwater; validate classification and acquisition timing.
Discover sources with MCP
Add this server to an HTTP-compatible MCP client, then call get_source to inspect the first source. The tool call is shown separately from the client configuration. Review upstream access details before requesting data.
Sentinel Data · Copernicus Contributing Missions · Federated Datasets · Complementary Data
Coverage llm-inferred
Global
Access docs-verified
Free and open datasets; free account with OAuth sign-in (per catalogue entry); quotas and limitations depend on user type and plan; Copernicus licence
Useful for
Use cases llm-inferred
Flood extent mapping from Sentinel-1 SAR imagery to support FRA evidence · Pre- and post-event change detection for site screening and geotechnical or landslide review · Vegetation index time series from Sentinel-2 for BNG habitat baseline mapping
Quotas and limitations depend on user type and plan · No UK-specific coverage or update cadence stated in the docs · Use is governed by the Copernicus licence
MCP server exposing Google Earth Engine as a set of MCP tools. These include: dataset discovery, metadata extraction, analysis primitives, and AI-assisted GEE Python code generation.
What's in it
Data offered docs-verified
Google Earth Engine dataset catalogue (list_datasets) · satellite image download · spectral indices (NDVI, NDWI, custom band math) · zonal statistics · temporal composites · area meeting a threshold condition · multi-period comparisons
Coverage llm-inferred
Global
Formats docs-verified
MCP stdio · Markdown (dataset info) · JSON-RPC · XML result shape · Mermaid graph · GEE Python code
Access docs-verified
Open source, MIT licence. Requires a Google Earth Engine project (GEE_PROJECT) with Earth Engine credentials, and an LLM provider API key (Anthropic, Google/Gemini or Vertex AI, or OpenAI) for the AI code-generation tools. Local install via Poetry. Cost not stated in docs.
Useful for
Use cases llm-inferred
Catchment land cover and forest cover percentage for flood risk assessment inputs · Flood extent area from imagery using threshold_area for post-event mapping · NDVI/NDWI change between periods via multi_period_analysis for ecological baselines · Zonal statistics of DEM or land cover over a catchment for morphometry
Requires Google Earth Engine authentication and a GEE project ID · AI code-generation tools need an LLM provider API key · Local install required (Poetry, clone repo); Python 3.11 to 3.14 · Catalogue auth field lists only LLM keys and omits GEE credentials · AI-generated GEE code and factuality outputs need expert verification · No rate limits, SLA or maintenance status stated in docs
STAC collection search and metadata (search_collections, get_collection) · STAC item search with spatial, temporal and attribute filters (search_items) · item detail and asset metadata (get_item) · queryables, aggregations and capability discovery · data size estimate for STAC items (estimate_data_size)
Coverage llm-inferred
Global per ENTRY regions; actual coverage depends on the STAC catalog queried
Formats docs-verified
JSON · text · MCP stdio
Access llm-inferred
Free/open source. No credentials documented in ENTRY auth. Licence conflict: ENTRY states MIT, DOCS state Apache 2.0.
Useful for
Use cases llm-inferred
Find Sentinel-2 or Landsat scenes over a UK catchment bbox for pre- and post-flood imagery · Estimate download size of satellite tiles before pulling data for a BNG habitat baseline · Locate DEM or land-cover STAC collections for drainage strategy catchment analysis, where the target catalog hosts them
Results depend entirely on the STAC catalog queried; no catalog is bundled · get_queryables returns an empty set if the catalog does not implement the endpoint · get_aggregations returns a message rather than data if the catalog does not support aggregation · sign_assets only applies to Microsoft Planetary Computer catalogs · estimate_data_size depends on odc.stac and XArray and falls back to estimates when odc.stac fails · Install URL in DOCS (wayfinder-foundry/stac-mcp) differs from ENTRY url (BnJam/stac-mcp); confirm the source repository before use · DOCS refer to a planned FastMCP integration (issues #69, #78) that is not yet delivered · No authentication is documented, so private or authenticated STAC APIs are not covered
Which listed sources support Satellite flood extent MCP?
Start with Copernicus Data Space Ecosystem, Google Earth Engine MCP, STAC MCP. Discover pre-event and post-event imagery and compare it with archived flood extents.
What should I check before using these sources for satellite flood extent mcp?
Observed surface water is not automatically floodwater; validate classification and acquisition timing.
How do I inspect Copernicus Data Space Ecosystem through MCP?
Connect an MCP client to https://terramcp.earth/mcp and call get_source with slug copernicus-data-space-ecosystem. Review its provenance, access requirements and limitations before contacting the upstream service. Discovery does not make every listed source directly executable through the router.
Are these sources free and independently verified?
Access and licence terms belong to each upstream provider; some products require registration or payment. Open each listing for evidence, health status and enrichment provenance. A wrapper and its underlying API can refer to the same dataset and are not independent observations.