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HarshShinde0

GeoCroissant MCP Server

by HarshShinde0

search_eo_scenes

Find satellite scenes in a bounding box on Earth Search, filtering by time, cloud cover, and collection to check data availability and return scene details.

Instructions

Searches satellite SCENES inside a bounding box on Earth Search.

    Executes a real STAC item search (pystac-client) against
    https://earth-search.aws.element84.com/v1 filtered by spatial extent,
    time range and cloud cover. Scenes are the individual acquisitions
    (tiles/granules) that become records of a GeoCroissant dataset.

    Usage: Use after `search_eo_datasets` (or directly with known
    collections) to check actual data availability for an area of interest.
    Feed promising results into `create_geocroissant_from_stac`.

    Returns:
    --------
    Dictionary containing:
        - scene_count and scenes: Per-scene id, collection, acquisition
          datetime, platform, cloud cover, native EPSG and asset keys.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bboxYesBounding box as [min_lon, min_lat, max_lon, max_lat].
limitNoMax scenes returned (1-50).
modalityNoModality used to pick default collections.
collectionsNoExplicit STAC collection ids. Defaults to curated collections for the chosen modality.
datetime_rangeNoSTAC datetime interval, e.g. "2023-06-01/2023-09-30".
max_cloud_coverNoMaximum eo:cloud_cover percentage for optical scenes (ignored when None).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It transparently states it executes a real STAC item search via pystac-client against the Earth Search endpoint and lists the filtering by spatial, temporal, and cloud-cover criteria. It also summarizes the return payload. It does not fully detail error cases or rate limits, but for a read-only search tool these are minor gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and efficiently front-loads the purpose, then usage context, and then return format. Each sentence adds value, with no repeated schema details or fluff. The Usage and Returns sections make it easy for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists and all six parameters are documented in the input schema, this description provides sufficient high-level context. It explains what the tool does, the external service it reads, the intended workflow position, and the fields in the result. Nothing critical for selection or invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds limited value for parameter semantics but does map the filter concepts (spatial extent, time range, cloud cover) to the overall API behavior and mentions the curated default collections via 'modality'. It doesn't enrich individual parameter fields beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the operation ('Searches satellite SCENES inside a bounding box'), identifies the resource ('satellite scenes' vs datasets), and clarifies context by distinguishing them as individual acquisitions. It separates itself from sibling search_eo_datasets and routes to the create_geocroissant_from_stac.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly places the tool in a workflow: 'Use after search_eo_datasets ... to check actual data availability' and 'Feed promising results into create_geocroissant_from_stac'. This tells the agent when to use it and points to the related data availability tool, giving clear when/when-not context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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