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tharlestsa

OpenLandMap MCP Server

by tharlestsa

get_collection_schema

Retrieve complete metadata schema for OpenLandMap environmental datasets, including asset definitions, spatial-temporal extent, and related documentation links.

Instructions

Return the full schema of a collection including asset definitions.

Provides complete metadata: asset types with MIME types and roles, STAC extensions, spatial/temporal extent, contact info, style URLs, related links (DOI, documentation, thumbnail).

Args: collection_id: Collection identifier (e.g. 'organic.carbon_usda.6a1c'). Use list_collections() to discover available IDs.

Returns: CollectionSchema dict with all metadata fields.

Example: get_collection_schema("organic.carbon_usda.6a1c")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collection_idYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns metadata (a read operation) and implies it's non-destructive, but lacks details on rate limits, authentication needs, or error behaviors. The description adds some context about the metadata scope but does not fully compensate for the absence of annotations.

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 front-loaded, starting with the core purpose followed by metadata details, parameter explanation, return value, and an example. Every sentence adds value without redundancy, making it efficient and easy to parse.

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

Completeness4/5

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

Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is largely complete, covering purpose, usage, parameters, and return format. However, it lacks explicit behavioral details (e.g., error handling or performance), which slightly reduces completeness for a tool with no annotations.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It provides detailed semantics for the single parameter (collection_id), including an example format and guidance to use list_collections() for discovery. This adds significant meaning beyond the basic schema, fully documenting the parameter's use and constraints.

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 tool's purpose with specific verbs ('Return the full schema') and resources ('collection including asset definitions'), distinguishing it from siblings like list_collections (which lists IDs) or get_item_detail (which focuses on items). It explicitly mentions what metadata is included, making the scope unambiguous.

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 provides clear context for when to use this tool by specifying it returns 'complete metadata' and referencing list_collections() to discover IDs. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., catalog_info or compare_collections), which prevents a perfect score.

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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