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UK Data: Airport weather (METAR and TAF)

taf_forecast

Read-onlyIdempotent

The current Terminal Aerodrome Forecast (TAF) for an airport: the official 24-30 hour forecast for wind, visibility, weather and cloud at the airfield, decoded into time blocks with plain-English change types (temporary, becoming, 30% chance). Answers "will it be foggy or windy at the airport tomorrow morning". Source: NOAA/NWS Aviation Weather Center (worldwide airports that issue TAFs). Covers the airfield only, not the route; not for flight planning. (cost: 1 credit)

Input Schema

NameRequiredDescriptionDefault
icaoNostring, required, one ICAO airport code (e.g. EGLL)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

The description adds rich behavioral context beyond the annotations. It details that the forecast is 'decoded into time blocks with plain-English change types (temporary, becoming, 30% chance),' giving the agent insight into the format of the output. It also defines the geographical and data source constraints (NOAA/NWS, worldwide TAF-issuing airports).

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 highly efficient, front-loading the core function while embedding vital operational constraints (airfield only, not for planning) near the end. Every sentence contributes unique value to the agent's decision-making process.

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 its single-parameter input, the description is maximally complete. It covers the data source, the forecast scope, the specific time window (24-30 hours), the output format complexity, and explicit limitations regarding routing and operational use.

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?

Since schema coverage is 100%, the description focuses on reinforcing the necessary context for the 'icao' parameter. It implicitly tells the agent that the provided ICAO must correspond to an airport that officially issues TAFs, adding value to the schema definition.

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 that the tool provides a Terminal Aerodrome Forecast (TAF), which is an official 24-30 hour forecast for specific weather and wind conditions. By defining TAF, it successfully distinguishes its specialized purpose from general weather tools like metar_latest or wind_forecast in the sibling list.

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?

It provides highly specific guidance by telling the agent what the tool answers ('will it be foggy or windy at the airport tomorrow morning'). Crucially, it also dictates when *not* to use the tool, stating it 'Covers the airfield only, not the route' and 'not for flight planning.'

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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