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kthys

irm-kmi-mcp Belgium Weather

Rain Forecast

rain_forecast

Get short-term rain nowcast for any Belgian location using official radar data. See expected precipitation every 10 minutes for the next 5 hours to plan around upcoming rain.

Instructions

Return the short-term rain nowcast for a location in Belgium.

Based on the IRM rain radar: expected precipitation per 10-minute frame over roughly the next 5 hours. Answers questions such as "will it rain in the next hour?" much more cheaply than the hourly forecast.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
communeNoMunicipality name, e.g. "Namur", "Bruxelles".
languageNoLanguage for the unit and official hint: "fr", "nl", "en" or "de".en
latitudeNoWGS84 latitude; use with ``longitude`` instead of ``commune``.
longitudeNoWGS84 longitude; use with ``latitude`` instead of ``commune``.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv1.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden. It discloses the data source (IRM rain radar), output granularity (per 10-minute frame), forecast horizon (~5 hours), and a performance trait (cheap relative to hourly forecast). It does not detail limitations such as accuracy or availability, but the output schema likely covers return shape.

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 tightly written: the core purpose appears in the first sentence, followed by useful context on data source, temporal resolution, and cost. Every sentence contributes value, and there is no filler or repetition of schema details.

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 tool is a read-style forecast lookup, and the description covers what it returns, how it works, its horizon, and when to prefer it. Parameter semantics are fully handled by the schema, and an output schema exists. An agent has enough context to select and call this tool 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?

Schema description coverage is 100%, so the baseline of 3 applies. The description does not add parameter-specific meaning beyond the schema, but that is acceptable because the schema already documents commune, language, latitude, and longitude with examples and mutual-exclusion guidance.

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-resource pairing: 'Return the short-term rain nowcast for a location in Belgium.' It further distinguishes itself from siblings by emphasizing radar-based 10-minute frames and a ~5-hour horizon, clearly separating it from hourly_forecast and other tools.

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 gives concrete usage context: it answers questions like 'will it rain in the next hour?' and explicitly notes it is 'much more cheaply than the hourly forecast.' This implies when to choose it over hourly_forecast, though it does not explicitly mention the other sibling tools or state exclusion criteria for daily or longer-range forecasts.

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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