MCP server

Umbra open SAR archive

io.github.reesehammer/umbra-mcp

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Search, preview, and measure Umbra open SAR from any MCP client. Umbra ships no search API.

Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

19

Token cost

About 10,101 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http umbra-mcp https://api.umbra-py.space/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.umbra-mcp]
url = "https://api.umbra-py.space/mcp"

Gemini CLI

{"mcpServers":{"umbra-mcp":{"httpUrl":"https://api.umbra-py.space/mcp"}}}

Cursor

{"mcpServers":{"umbra-mcp":{"url":"https://api.umbra-py.space/mcp"}}}

VS Code

{"servers":{"umbra-mcp":{"type":"http","url":"https://api.umbra-py.space/mcp"}}}

Antigravity

agy mcp add umbra-mcp https://api.umbra-py.space/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"umbra-mcp","server_url":"https://api.umbra-py.space/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"umbra-mcp","server_url":"https://api.umbra-py.space/mcp"}

Tools

• change_composite ~279 tokens
  Composite 2-3 passes of one site into a change image. Pass the STAC URLs of two or three acquisitions of the *same* site in time order. Colors encode change: bright green = signal appeared after the first date, magenta = signal vanished, grey = unchanged. Refuses to mix polarization
• describe_scene ~541 tokens
  Return a SAR-literate reading of the scene at ``url``. The picture and the metadata card are produced deterministically; a model only interprets them. How that model is reached depends on whether this process holds a vision key: - **No key** (the public community host): this tool d
• download_asset ~601 tokens
  Download one asset of an item to local disk, gated by a size check. SAR products can be multiple GB, so this is a two-step handshake: call it first with ``confirm=False`` (the default) to get the byte size without downloading, then again with ``confirm=True`` to actually fetch. Returns
• find_repeat_sites ~1280 tokens
  Rank the archive's most repeat-imaged sites — where change detection has something to measure. The discovery step *before* every analysis verb. ``change`` / ``timescan`` / ``stack`` / ``pick_change_interval`` / ``narrate_change`` all answer *what* changed at a site, but each assumes
• find_similar ~473 tokens
  Find archived scenes that **look like** the acquisition at ``url``. This is the one search that lives in the pixels, not the metadata: where ``search_catalog`` filters by date/bbox/task-name, this renders the query item's quicklook, embeds it, and ranks the pre-embedded scene index by v
• find_similar_text ~345 tokens
  Find archived scenes matching a plain-language ``query`` over their imagery. Text-to-scene search: embeds ``query`` ("ships at a berth", "a flooded field") and ranks the stored *image* vectors by cosine similarity. It only works when the index was built and this query is embedded with t
• geocode_place ~132 tokens
  Resolve a free-text place name to a bounding box via Nominatim. Returns ``{"bbox": [min_lon, min_lat, max_lon, max_lat], "display_name": ...}`` so the agent can inspect the box before searching, or pass it straight to ``search_catalog``'s ``bbox``.
• get_item ~573 tokens
  Return the full context card for one item. ``url`` is the JSON URL of an open-data STAC sidecar, a GEC/SICD (or other product) filename such as ``2025-05-17-17-43-46_UMBRA-08_GEC.tif``, or the acquisition stem ``2025-05-17-17-43-46_UMBRA-08``. A filename or stem is resolved through
• index_stats ~226 tokens
  Report on the local catalog index, if one has been built or fetched. Lets a long-running agent decide whether searches will be instant (index present) or pay a live S3 walk, and how stale the snapshot is. ``end`` is the newest acquisition that is not ``datetime_suspect``. ``datetime
• narrate_change ~432 tokens
  Return a grounded reading of *what changed* between passes. Sibling of :func:`describe_scene`. The composite and the per-block dB grid are produced deterministically; a model only narrates change those numbers support. Pass the STAC URLs in time order; mixed polarizations are refused.
• pick_change_interval ~738 tokens
  Scan a site's whole series and name the two passes worth narrating. The deterministic first half of the "watch a site over time" story, and the step that turns a wall of passes into an answer. Given the STAC URLs of a site's passes (2 or more, any order), it co-registers them into a
• quicklook ~373 tokens
  Return a scene's quicklook PNG as an image block. Prefers a baked catalog preview (``preview="auto"``, the default) so a public host can show the radar scene without proxying Umbra COGs. **A baked preview is typically 512 px** (the published weekly sidecar). ``max_size`` applies onl
• search_catalog ~1086 tokens
  Search Umbra's catalog and return compact context cards. Filters (all optional, combine freely): ``bbox`` as ``[min_lon, min_lat, max_lon, max_lat]`` in WGS84 degrees; ``intersects`` as a GeoJSON polygon object (a ``Polygon`` / ``MultiPolygon`` geometry, or a ``Feature`` / ``Feature
• stack_provenance ~616 tokens
  Check whether a site's passes are one measurement, before measuring them. ``stack_stats`` refuses a selection whose rasters were made by *different* ``umbra convert`` settings — a calibrated pass differenced against an uncalibrated one puts the difference between two conversions on the
• stack_stats ~852 tokens
  Measure how much a site changed across its passes, as numbers. The quantitative counterpart to ``change_composite`` / ``timescan``: those return a picture of *where* change is, this returns the *how much*. Pass the STAC URLs of a site's passes (2 or more, any order); they are co-registe
• stamp_description ~337 tokens
  Validate a client-produced scene reading and stamp provenance. This is the interpretation boundary for the no-key kit path: you read the picture ``describe_scene`` returned, produce ``{summary, observed_features, confidence, caveats}``, and pass it here. The library checks the shape
• stamp_narration ~261 tokens
  Validate a client-produced change narration and stamp provenance. Sibling of :func:`stamp_description` for the no-key ``narrate_change`` kit. Pass the JSON you produced plus the ``change_stats`` grid from the kit so the stamped object carries the numbers the prose was grounded in.
• timescan ~187 tokens
  Summarize a whole time series into one activity image. Pass the STAC URLs of a site's passes; the timescan encodes per-pixel temporal statistics so bright/colored areas are *where activity happened* over the series. Returns the image block plus attribution.
• watch_site ~769 tokens
  Report only the acquisitions **new** since the last check of this site. SAR's value for monitoring is its cadence, so the natural workflow is *standing*: run the same search each time and act only on what changed. This tool packages that idempotent delta — call it in one turn, come back

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