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HarshShinde0

GeoCroissant MCP Server

by HarshShinde0

create_geocroissant_scaffold

Generate a standards-conformant GeoCroissant JSON-LD scaffold that passes official validation, with spatial coverage, band configuration, and RecordSet wiring.

Instructions

Generates a validated GeoCroissant JSON-LD scaffold from parameters.

    Produces a standards-conformant starting point modeled on the official
    GeoCroissant example: correct @context (including the `geocr` prefix),
    dual conformance (`croissant/1.1` + `geocr`), schema.org spatial/temporal
    coverage, GeoCroissant properties (CRS, resolutions, band configuration,
    spectral bands), distribution entries and a RecordSet wired to them via
    proper cr:source/cr:extract declarations.

    The generated document is then parsed and checked by the real
    ``mlcroissant`` validator, so `valid=True` means the scaffold already
    passes the official library checks.

    Usage: Call FIRST when creating new dataset metadata, then edit the
    returned JSON-LD for domain specifics and re-check with
    `validate_croissant`. Use `inspect_geocroissant` afterwards to review it.

    Returns:
    --------
    ScaffoldResult containing:
        - valid: Whether the scaffold passed mlcroissant validation.
        - json_ld: The generated document.
        - errors/warnings: Library messages when not fully clean.
        - path: Output file path when output_filename was given.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bboxNoSpatial coverage as [min_lon, min_lat, max_lon, max_lat] in EPSG:4326 (standard GIS order).
nameYesName of the dataset.
licenseNoLicense URL, preferably SPDX, e.g. "https://creativecommons.org/licenses/by/4.0/".
versionNoDataset version, e.g. "1.0".
creatorsNoCreator names (rendered as sc:Organization entries).
file_setsNoList of FileSet specs: {"id": "images", "name": "Images", "encoding_format": "image/tiff", "includes": "images/**/*.tif"}.
band_namesNoOrdered raster band names, e.g. ["Blue","Green","Red","NIR"].
field_nameNoName of the main data Field inside the RecordSet.data
descriptionNoDescription of the dataset.
file_objectsNoList of FileObject specs: {"id": "data.zip", "name": "data.zip", "content_url": "https://...", "encoding_format": "application/zip", "sha256": "...", "md5": "..."}.
date_publishedNoPublication date as YYYY-MM-DD.
field_is_arrayNoWhether the main Field is an array (raster/tensor).
spectral_bandsNoPer-band spectral metadata entries like {"name": "Blue", "centerWavelength": {"value": 490, "unitText": "nm"}, "bandwidth": {...}}.
field_data_typeNoField dataType, e.g. "sc:ImageObject", "sc:Text", "sc:URL".sc:ImageObject
output_filenameNoWhen provided, writes the validated JSON-LD to this filename inside GEOCR_OUTPUT_DIR (or the system temp dir) and returns the path.
record_set_nameNoName/@id of the scaffolded RecordSet.records
field_array_shapeNoArray shape as comma-separated dims, e.g. "512,512,6" (requires field_is_array=True).
temporal_coverageNoTemporal coverage interval, e.g. "2018-01-01/2021-12-31".
source_file_set_idNo@id of the FileSet/FileObject the main Field reads from. Defaults to the first declared distribution entry.
spatial_resolutionNoGround sampling distance value (with unit below).
record_set_descriptionNoDescription of the scaffolded RecordSet.
spatial_resolution_unitNoUnit for spatial_resolution, e.g. "m".m
temporal_resolution_unitNoUnit for temporal cadence, e.g. "days".days
temporal_resolution_valueNoRevisit cadence value (with unit below).
coordinate_reference_systemNoCRS identifier, e.g. "EPSG:4326".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoFile path when the scaffold was written to disk.
validYesWhether the generated JSON-LD passes mlcroissant validation.
errorsNoValidation errors on the generated file.
json_ldYesThe generated JSON-LD document.
warningsNoValidation warnings on the generated file.
Behavior5/5

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

There are no annotations, so the description carries full responsibility, and it delivers: it explains the scaffold passes mlcroissant validation, what valid=True means, that errors/warnings may be returned when not fully clean, and that an output file path is provided when output_filename is used. This is more than what annotations alone would have provided.

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

Conciseness4/5

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

The description is well-structured with a clear purpose, detail section, usage instruction, and Returns list. It is somewhat long, and the Returns section overlaps with the existing output schema, so a small amount of redundancy prevents a 5.

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?

For a highly parameterized tool with 25 parameters, the description is complete: it explains the generation workflow, validation guarantees, how to continue after creation, what the result contains, and the optional file-writing side effect. An agent has enough context to call and use the tool correctly.

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?

All 25 parameters have schema descriptions, so the input schema already carries most semantic weight. The tool description adds context about how parameters map into the generated JSON-LD (CRS, resolutions, bands, RecordSet wiring), but it does not explain individual parameters beyond the schema. This matches the baseline for a schema with complete coverage.

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 opens with a specific verb-resource pair: 'Generates a validated GeoCroissant JSON-LD scaffold from parameters.' It clearly differentiates from the STAC-based sibling by emphasizing 'from parameters' and stating the output is modeled on the official GeoCroissant example and validated by mlcroissant.

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

Usage Guidelines4/5

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

The description gives a clear workflow: call this first when creating new dataset metadata, then edit the scaffold and re-check with validate_croissant, and review with inspect_geocroissant. However, it does not explicitly mention the main alternative sibling create_geocroissant_from_stac or state when someone should use that instead.

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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