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Bigred97

au-weather-mcp

search_locations

Fuzzy-search Australian city names and state codes to look up location IDs for weather data. Returns ranked results for curated locations.

Instructions

Fuzzy-search the 45 curated Australian locations.

The curated set covers all 8 state/territory capitals plus 37 major regional centres (every AU population centre over ~25k). Anything outside the curated set still resolves via place-name geocoding or postcode lookup — see list_curated() for the full set.

Examples: results = await search_locations("sydney") # → [{id: 'sydney', name: 'Sydney', state: 'NSW', ...}]

results = await search_locations("nsw")
# → Newcastle, Wollongong, Sydney (all NSW locations)

When to use: - Discover the location ID for a city you know by name - Find all supported locations in a state - Verify whether a place is in the curated set before calling get_weather

Returns: List of LocationSummary (id, name, state, description), ranked by relevance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return, ranked by relevance.
queryYesFree-text search query. Matches against location IDs, names, and state codes. Case-insensitive.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It discloses fuzzy-matching behavior, coverage of curated set, resolution for non-curated places, and return format. It could note that it's read-only, but the examples imply no side effects.

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-organized with clear sections, bullet points, and examples. Every sentence is informative and earns its place, avoiding unnecessary fluff.

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 the output schema exists, the description appropriately summarizes return format (List of LocationSummary with id, name, state, description). It integrates well with sibling tools and provides enough context for an agent to decide when to use this tool.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by providing examples (e.g., 'tropical north') and clarifying matching behavior (case-insensitive, matches IDs, names, state codes), which goes beyond the schema's description.

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 does 'Fuzzy-search the 45 curated Australian locations.' It specifies the verb and resource accurately and distinguishes from siblings like list_curated (which returns the full set) and describe_location.

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?

The description includes a 'When to use' section with three specific use cases: discovering location ID, finding all locations in a state, and verifying if a place is curated. It also mentions alternatives for outside the curated set, referencing list_curated() and geocoding.

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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