Umbra open SAR archive

Instalación

Elige la app de IA que usas y copia la configuración.

Pasó 5/5 revisiones · Lo que preguntas pasa por el servidor del autor (api.umbra-py.space) · ✓ Comandos revisados el 13 nov 2026

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

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

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

claude mcp add --scope user umbra --env UMBRA_CANOPY_TOKEN=your-value --env UMBRA_INDEX_DB=your-value --env ANTHROPIC_API_KEY=your-value --env OPENAI_API_KEY=your-value -- uvx umbra-py

{
  "mcpServers": {
    "umbra": {
      "command": "uvx",
      "args": [
        "umbra-py"
      ],
      "env": {
        "UMBRA_CANOPY_TOKEN": "your-value",
        "UMBRA_INDEX_DB": "your-value",
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

{
  "mcpServers": {
    "umbra": {
      "command": "uvx",
      "args": [
        "umbra-py"
      ],
      "env": {
        "UMBRA_CANOPY_TOKEN": "your-value",
        "UMBRA_INDEX_DB": "your-value",
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

{
  "servers": {
    "umbra": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "umbra-py"
      ],
      "env": {
        "UMBRA_CANOPY_TOKEN": "your-value",
        "UMBRA_INDEX_DB": "your-value",
        "ANTHROPIC_API_KEY": "your-value",
        "OPENAI_API_KEY": "your-value"
      }
    }
  }
}

Qué apps pueden usarlo

Según cómo se conecta este servidor y la documentación de cada app, comprobado el 10 nov 2026.

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Se ejecutan en tu computadora en

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Todas las apps, con ventajas y desventajas →

Qué puede hacer

Nombres y descripciones de las herramientas tal como los informó el servidor el 12 nov 2026. Los escribió el creador, no nosotros; los mostramos como texto sin formato.

• search_catalog

  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 object (a ``Point``, ``Polygon``, or ``MultiPolygon``, or a ``Feature`` / ``FeatureC

• find_repeat_sites

  Rank sites imaged more than once, before a change tool is pointed at one. Use this when the site is not already known. Each site's ``hrefs`` are that site's STAC URLs, oldest first, ready for ``pick_change_interval``, ``change_composite``, or ``stack_stats``. Prefer this, or ``se

• get_item

  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 throu

• geocode_place

  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``.

• index_stats

  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``. ``datet

• quicklook

  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

• change_composite

  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 polarizat

• timescan

  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.

• stack_stats

  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-

• stack_provenance

  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

• pick_change_interval

  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 int

• download_asset

  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 fetch it. On a lo

• watch_site

  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, co

• find_similar

  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 ind

• find_similar_text

  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

• describe_scene

  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

• narrate_change

  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 ref

• stamp_description

  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 sh

• stamp_narration

  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

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 13 nov 2026

• ✓ Superada

  El creador coincide con el repo

  El nombre en el registro se verificó con la cuenta de GitHub reesehammer, dueña del repo

• ✓ Superada

  Sigue en mantenimiento

  Último cambio de código: ayer

• ✓ Superada

  Tiene licencia

  Usa Apache-2.0

• ✓ Superada

  El endpoint responde

  Respondió en 185 ms (HTTP 200)

• – No aplica

  Inicio de sesión con OAuth

  No hace falta iniciar sesión

• ✓ Superada

  Sin instrucciones ocultas en las descripciones de herramientas

  Leímos 19 descripciones de herramientas y no encontramos instrucciones ocultas para la IA

Reseñas de usuarios

Aún no hay reseñas

Inicia sesión con Google para escribir una reseña

Comparado con otros en Búsqueda y memoria de IAi

• Más estrellas que el 82% de los otros 6020 servidores de esta categoría
• Publicado en el registro más recientemente que el 68% de los otros 6020 servidores de esta categoría
• Revisiones básicas iguales o mejores que el 100% de los otros 6020 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Otros de la misma categoría

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:↓ Descargas por semanai★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

Context7

Up-to-date docs for AI agents

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Codebase Memory

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

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Ruflo

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de ruvnet·Muy usado·↓ 74,3 mil/sem·★ 81,6 mil Estrellas de todo el repositorio·Actualizado anteayer

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Instalar

README del creador

pip install umbra-py              # core: search + download + metadata

pip install "umbra-py[viz,load]"

from umbra_py import CatalogIndex, to_xarray

from umbra_py import UmbraCatalog

# Claude Code

{

uvx --from 'umbra-py[mcp]' umbra-mcp

{

@software{hammerstrom2026umbrapy,