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Bigred97

au-weather-mcp

list_curated

Retrieve the 21 curated Australian location IDs including all state capitals and major regional centres for weather queries.

Instructions

List the 21 curated Australian location IDs supported by this MCP.

The curated set covers all 8 state/territory capitals plus 13 major regional centres: - 8 capitals: sydney, melbourne, brisbane, perth, adelaide, hobart, darwin, canberra - 5 NSW regional: newcastle, wollongong (NSW capitals as above) - 5 QLD regional: gold_coast, sunshine_coast, cairns, townsville, mackay - 3 VIC regional: geelong, ballarat, bendigo - 1 TAS regional: launceston - 2 remote: alice_springs (NT), broome (WA)

Example: ids = list_curated() # → ['adelaide', 'alice_springs', 'ballarat', 'bendigo', 'brisbane', # 'broome', 'cairns', 'canberra', 'darwin', 'geelong', 'gold_coast', # 'hobart', 'launceston', 'mackay', 'melbourne', 'newcastle', # 'perth', 'sunshine_coast', 'sydney', 'townsville', 'wollongong']

When to use: - You want to know which locations have first-class support - You're building a UI that shows the supported set up front - You want to plan a multi-location dashboard call

Returns: Sorted list of location IDs. Always 21 entries today; adding a location is a YAML edit, not a code change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler for list_curated. Decorated with @mcp.tool, it returns the sorted list of curated location IDs by delegating to curated_mod.list_ids().
    @mcp.tool
    def list_curated() -> list[str]:
        """List the 21 curated Australian location IDs supported by this MCP.
    
        The curated set covers all 8 state/territory capitals plus 13 major
        regional centres:
            - 8 capitals: sydney, melbourne, brisbane, perth, adelaide, hobart,
              darwin, canberra
            - 5 NSW regional: newcastle, wollongong (NSW capitals as above)
            - 5 QLD regional: gold_coast, sunshine_coast, cairns, townsville, mackay
            - 3 VIC regional: geelong, ballarat, bendigo
            - 1 TAS regional: launceston
            - 2 remote: alice_springs (NT), broome (WA)
    
        Example:
            ids = list_curated()
            # → ['adelaide', 'alice_springs', 'ballarat', 'bendigo', 'brisbane',
            #    'broome', 'cairns', 'canberra', 'darwin', 'geelong', 'gold_coast',
            #    'hobart', 'launceston', 'mackay', 'melbourne', 'newcastle',
            #    'perth', 'sunshine_coast', 'sydney', 'townsville', 'wollongong']
    
        When to use:
            - You want to know which locations have first-class support
            - You're building a UI that shows the supported set up front
            - You want to plan a multi-location dashboard call
    
        Returns:
            Sorted list of location IDs. Always 21 entries today; adding a
            location is a YAML edit, not a code change.
        """
        return curated_mod.list_ids()
  • The list_ids() helper function that loads the curated registry (cached in _REGISTRY) and returns sorted keys. This is the actual data-loading logic behind list_curated.
    def list_ids() -> list[str]:
        """Return all curated location ids, sorted alphabetically."""
        global _REGISTRY
        if _REGISTRY is None:
            _REGISTRY = _load()
        return sorted(_REGISTRY.keys())
  • Module-level _REGISTRY cache variable that stores the loaded CuratedLocation dict.
    _REGISTRY: dict[str, CuratedLocation] | None = None
  • The _load() function that reads locations.yaml and parses it into the CuratedLocation dict. The YAML is the source of truth for the 21 curated locations.
    def _load() -> dict[str, CuratedLocation]:
        raw = yaml.safe_load(_yaml_path().read_text(encoding="utf-8"))
        locs = raw.get("locations") or {}
        out: dict[str, CuratedLocation] = {}
        for loc_id, fields in locs.items():
            if not _LOCATION_ID_PATTERN.match(loc_id):
                raise ValueError(
                    f"Location id {loc_id!r} must match {_LOCATION_ID_PATTERN.pattern}"
                )
            out[loc_id] = CuratedLocation(
                id=loc_id,
                name=str(fields["name"]),
                state=str(fields["state"]),
                description=fields.get("description"),
                latitude=float(fields["latitude"]),
                longitude=float(fields["longitude"]),
                timezone=str(fields["timezone"]),
                elevation_m=(float(fields["elevation_m"]) if "elevation_m" in fields else None),
                nearest_bom_station=fields.get("nearest_bom_station"),
            )
        return out
  • The LocationSummary model used for both search_locations and list_curated results (surface-level location info).
    class LocationSummary(BaseModel):
        """A curated AU location — surface for search_locations and list_curated."""
        id: str
        name: str
        state: str  # NSW, VIC, etc.
        description: str | None = None
Behavior4/5

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 output is a sorted list, always 21 entries today, and adding a location is a YAML edit. This provides stability and extensibility information.

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 with sections, an example, and a 'Returns' note. Every sentence adds value, and it is concise without being overly lengthy.

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?

The description fully covers the tool's purpose, return format, and usage context. Given the presence of an output schema, the description sufficiently complements it.

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?

There are no parameters, and schema coverage is 100%. The description adds no parameter info, but none is needed. Baseline 4 is appropriate as the description is complete for a parameterless tool.

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 it lists the 21 curated Australian location IDs. It uses specific verb 'list' and resource 'curated Australian location IDs', and differentiates from sibling tools like 'search_locations'.

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 explicitly states when to use: to know which locations have first-class support, for building UI, or planning multi-location dashboard call. It provides clear context, though it does not explicitly mention when not to use.

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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