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Smarter Weather

by io.github.smarterweather

Search locations

search_locations
Read-onlyIdempotent

Resolve a place query to candidate locations with coordinates. Accepts city names ("Denver"), city+state ("Portland, OR" via query), ZIP codes ("50219"), or partial input with fuzzy=true for autosuggest-style matching ("bost" -> Boston). Returns ranked candidates with lat/lon. Most weather tools accept a location string directly and geocode internally -- use this tool only to disambiguate ("which Springfield?") or to present location choices to the user. Example: {"query": "Springfield"} returns all major Springfields ranked by place importance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fuzzyNoAutosuggest mode for partial/misspelled input. Default false (exact search).
limitNoMaximum candidates to return (1-10). Default 5.
queryYesPlace query: city, "city, state", ZIP, or partial text with fuzzy=true.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
candidatesYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / properties / candidates / items / additionalProperties
      Previous value: -falseNew value: +true
  2. Changed1 schema field changed
    • addedOutput schema / properties / candidates / items / properties / place_id
      Added value: +{
+  "type": "string"
+}
  3. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds behavioral detail beyond that: accepted query formats, fuzzy matching behavior, ranked candidates, and lat/lon output. It does not contradict annotations and gives useful operational context.

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 compact and front-loaded with the core purpose. Every sentence adds useful information: accepted formats, fuzzy behavior, output type, usage boundaries, and a concrete example. There is no filler or repetition.

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 rich input schema, output schema, and annotations, the description covers the remaining context an agent needs: when to invoke it, what inputs are accepted, what output shape to expect, and how it relates to sibling tools. Nothing critical is missing.

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 the baseline is 3. The description adds extra semantic value by giving concrete examples such as 'Portland, OR' via query, '50219', and 'bost' -> Boston with fuzzy=true, plus an example explaining that Springfields are ranked by importance. This goes beyond the schema's parameter descriptions.

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 and resource: 'Resolve a place query to candidate locations with coordinates.' It clearly states what the tool does and distinguishes it from siblings by noting that most weather tools geocode internally and this tool is only for disambiguation or presenting choices.

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 explicitly says 'use this tool only to disambiguate' or 'to present location choices to the user,' and contrasts with most weather tools that accept a location string directly. This gives an agent clear routing guidance and prevents unnecessary calls.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, and the detailed descriptions generally prevent misselection. A few near-overlapping pairs exist — get_current_conditions vs get_observations, and get_forecast already bundling current conditions, alerts, and outlooks — so some ambiguity remains.

Naming Consistency4/5

The overwhelming majority of tools follow a get_<object>_<modifier> pattern in snake_case, and the non-get tools still use an imperative verb_noun form. The mix of verbs (get, list, describe, find, query, search, reverse) is a minor inconsistency, but the overall pattern is predictable.

Tool Count2/5

At 32 tools, the surface is heavy and exceeds the 25+ threshold for a large tool set. The weather domain justifies much of the breadth, but several tools overlap in scope and could plausibly be consolidated, making the count feel higher than necessary.

Completeness5/5

The tool set comprehensively covers current conditions, forecasts, hourly data, climate, alerts, severe weather, air quality, tropical systems, upper-air soundings, maps, model data, geocoding, and platform status. There are no obvious dead-end workflows, and raw access via query_dataset fills most remaining gaps.

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