Umbra open SAR archive

インストール

使っているAIアプリを選んで、設定をコピーしてください。

チェック 5/5 合格 · 質問内容は提供元のサーバー（api.umbra-py.space）を通ります · ✓ コマンド確認済み 2026年11月13日

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"
      }
    }
  }
}

使えるアプリ

このサーバーのつなぎ方と、各アプリの公式ドキュメントに基づきます。2026年11月10日に確認。

インターネット経由でつなげるアプリ

ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast

パソコン上で動かせるアプリ

ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast

すべてのアプリと長所・短所 →

できること

ツールの名前と説明は、サーバーが2026年11月13日に返したそのままの内容です。書いたのは作者で、私たちではありません。プレーンテキストとして表示しています。

• 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

基本チェックi

誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月13日にチェック

• ✓ 合格

  作者とリポジトリが一致

  レジストリ名は、リポジトリを所有するGitHubアカウント reesehammer で確認済みです

• ✓ 合格

  今もメンテナンスされている

  コードの最終更新：昨日

• ✓ 合格

  ライセンスがある

  Apache-2.0を使用

• ✓ 合格

  エンドポイントが応答する

  37ミリ秒で応答（HTTP 200）

• – 対象外

  OAuthでサインイン

  サインイン不要

• ✓ 合格

  ツールの説明に隠れた指示がない

  19件のツールの説明を読み、AIへの隠れた指示は見つかりませんでした

ユーザーレビュー

まだレビューはありません

Google でログインしてレビューを書く

検索・AIの記憶のほかのサーバーとの比較i

• このカテゴリのほかの6045件のうち82%よりスターが多い
• このカテゴリのほかの6045件のうち68%より最近レジストリに公開
• 基本チェックの結果が、このカテゴリのほかの6045件のうち100%と同じかそれ以上

レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。

同じカテゴリのほかの候補

同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。

カードの見方：↓ 週あたりのダウンロードi★ GitHubスターi3つの柱：レビュー · チェック · 利用状況i更新＝最新バージョンをレジストリに公開した日i

Context7

Up-to-date docs for AI agents

作者：upstash·広く使われている·↓ 125万/週·★ 6.5万·昨日更新

パソコンでもクラウドでも

インストール

Codebase Memory

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

作者：DeusData·広く使われている·↓ 1.7万/週·★ 4.9万·27 日前更新

パソコンで動く

インストール

Ruflo

Agent teams, swarms, memory and hooks for Claude Code and Codex, served over MCP.

作者：ruvnet·広く使われている·↓ 7.4万/週·★ 8.2万 リポジトリ全体のスター·今日更新

パソコンで動く

インストール

作者のREADME

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,