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halizz821

Environment Canada Weather Alerts MCP Server

by halizz821

get_alert_details

Retrieve full text, metadata, and boundary geometry for a specific Canadian weather alert by feature ID, with optional French output.

Instructions

Retrieves full details including text description and boundary geometry for a specific alert.

Args: feature_id: Unique feature ID or item ID of the weather alert. language: Output language ('en' or 'fr'). Default is 'en'.

Returns: JSON string containing detailed alert metadata and polygon geometry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoen
feature_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the return shape (JSON string with alert metadata and polygon geometry), which tells the agent to expect geometry data, but it is silent on permissions, rate limits, whether the alert can expire or be unavailable, and error behavior for an unknown feature_id.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with a one-sentence purpose, then cleanly separated Args/Returns sections. Every line carries information, though the Returns section partially duplicates what the output schema already conveys.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read whose output schema already defines the response, the description supplies everything needed to invoke it correctly: both parameters are explained and the language enum is enumerated. Minor gaps are the provenance of feature_id and any failure mode for an invalid ID.

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 description coverage is 0%, so the description must compensate and largely does: feature_id is described as a unique feature or item ID of the alert, and language is documented with its permitted values ('en' or 'fr') and default ('en') — enum-like detail the schema itself lacks. It stops short of saying where a feature_id comes from, which is the one remaining semantic gap.

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?

Names a specific verb (retrieves) and resource (full alert details) with the scope explicitly spelled out: text description plus boundary geometry. Read against the sibling get_alert_summary, the word 'full' makes the distinction between the detail view and the summary view immediately apparent without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: you call this when you already have a feature_id and need the complete record. There is no explicit guidance on when to prefer this over get_alert_summary, get_weather_alerts, or get_alerts_near_coordinates, nor any stated prerequisites for obtaining a feature_id. Adequate but leaves the routing decision to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.