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get_conformance

Check server conformance classes to verify STAC API compliance and ensure compatibility with geospatial data standards.

Instructions

Return server conformance classes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The primary handler function that executes the get_conformance tool logic: retrieves STAC conformance data using the client and formats the output as markdown text or JSON based on arguments.
    def handle_get_conformance(
        client: STACClient,
        arguments: dict[str, Any],
    ) -> list[TextContent] | dict[str, Any]:
        check = arguments.get("check")
        data = client.get_conformance(check)
        if arguments.get("output_format") == "json":
            return {"type": "conformance", **data}
        conforms_to = data.get("conformsTo", [])
        num_classes = len(conforms_to)
        result_text = f"**Conformance Classes ({num_classes})**\n\n"
        if conforms_to:
            result_text += "conformsTo:\n"
            for class_uri in conforms_to:
                result_text += f"- `{class_uri}`\n"
        checks = data.get("checks")
        if checks:
            result_text += "\n**Checks**\n"
            for class_uri, satisfied in checks.items():
                result_text += (
                    f"- `{class_uri}`: {'Satisfied' if satisfied else 'Not Satisfied'}\n"
                )
        return [TextContent(type="text", text=result_text)]
  • Internal registry of tool handlers in execution.py, mapping 'get_conformance' to its handler function.
    _TOOL_HANDLERS: dict[str, Handler] = {
        "search_collections": handle_search_collections,
        "get_collection": handle_get_collection,
        "search_items": handle_search_items,
        "get_item": handle_get_item,
        "estimate_data_size": handle_estimate_data_size,
        "get_root": handle_get_root,
        "get_conformance": handle_get_conformance,
        "get_queryables": handle_get_queryables,
        "get_aggregations": handle_get_aggregations,
        "sensor_registry_info": handle_sensor_registry_info,
    }
  • MCP server-level registration of the get_conformance tool using FastMCP @app.tool decorator, delegating execution to the internal handler.
    @app.tool
    async def get_conformance() -> list[dict[str, Any]]:
        """Return server conformance classes."""
        return await execution.execute_tool(
            "get_conformance", arguments={}, catalog_url=None, headers=None
        )
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Return' implies a read-only operation, it doesn't specify whether this requires authentication, has rate limits, what format the conformance classes are returned in, or any other behavioral characteristics. The description is too minimal for a tool with no annotation coverage.

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 a single, efficient sentence that states exactly what the tool does with zero wasted words. It's appropriately sized for a simple retrieval tool and gets straight to the point without unnecessary elaboration.

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 that this is a zero-parameter tool with an output schema (which handles return value documentation), the description provides adequate context for its purpose. However, the lack of behavioral information and usage guidance relative to siblings leaves some gaps in completeness.

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

Parameters4/5

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

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description doesn't need to add parameter information, and it doesn't attempt to. A baseline of 4 is appropriate for zero-parameter tools.

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

Purpose4/5

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 a specific verb ('Return') and resource ('server conformance classes'), making it immediately understandable. However, it doesn't distinguish this tool from its siblings (like get_root, get_queryables, etc.) which also retrieve metadata about the server.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like get_root and get_queryables that also retrieve server metadata, there's no indication of what makes this tool distinct or when it should be preferred over other options.

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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