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WMO City Climate Normals

wmo-weather.climate.normals
Read-onlyIdempotent

Get 30-year monthly climate normals for a city — average max/min/mean temperature and rainfall for each of the 12 months, sourced from the WMO climatological standard normals (typically 1961-1990 or 1991-2020 depending on the station). Requires a WMO city ID (see wmo-weather.city_search first, e.g. 219 for Kabul). Useful for "what is the weather usually like in [city] in [month]" style questions, distinct from wmo-weather.forecast which covers the next 7 days. Data: worldweather.wmo.int, no auth required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
city_idYesWMO city ID, e.g. 219 (Kabul) or 183 (Tokyo). Discover IDs with wmo-weather.city_search.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only, open-world, idempotent, and non-destructive traits. The description adds useful context beyond those: the data source (worldweather.wmo.int), the fact that no auth is required, and the variant normals period (1961-1990 or 1991-2020 depending on station). It also discloses the prerequisite of the city ID. These enrich the agent's understanding without repeating the annotations, though it doesn't discuss rate limits or edge-case station data gaps, so a 4 is appropriate.

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: the first sentence states the core purpose, followed by source/period detail, then prerequisite and usage guidance. Every sentence earns its place, and there is no fluff. It's a model of efficient description writing.

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?

For a single-parameter, read-only tool with an output schema, the description is complete. It covers what it returns, how to obtain the parameter, when to use it, and how it differs from a sibling. The output schema handles return structure, so nothing needed is missing. It even notes auth is not required. This is fully adequate for an agent to call it correctly.

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

Parameters3/5

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

The input schema already documents city_id with examples and the instruction to use wmo-weather.city_search, so schema coverage is 100%. The description reiterates the need for the city ID and gives the Kabul example, but it adds no new meaning beyond the schema. With complete schema documentation, the baseline of 3 is correct; the description's reinforcement doesn't raise it.

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 states a specific verb ('Get') and resource ('30-year monthly climate normals for a city'), with explicit detail on the content (avg max/min/mean temperature and rainfall per month). It clearly differentiates from wmo-weather.forecast by naming the sibling and its 7-day scope, and references wmo-weather.city_search for ID lookup. No ambiguity remains about what this tool does.

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 tells when to use this tool ('what is the weather usually like in [city] in [month]' style questions), names the distinct alternative (wmo-weather.forecast covers next 7 days), and directs the user to call wmo-weather.city_search first to obtain the required city_id. This is actionable guidance that steers selection away from siblings.

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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