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IBM

chuk-mcp-open-meteo

by IBM

batch_interpret_weather_codes

Interpret multiple WMO weather codes in a single batch call to efficiently process weather data for multiple locations. Get icon URLs and interpretations with one request instead of separate calls.

Instructions

Interpret multiple WMO weather codes in a single call.

Instead of calling interpret_weather_code multiple times (one per code), pass all codes at once. This is much more efficient when processing weather data for multiple locations.

Args: weather_codes: Comma-separated WMO weather code integers (0-99). Example: "3,51,61,95" or "0, 3, 45, 80"

Returns: BatchWeatherCodeResponse: Pydantic model with: - results: List of interpretations in same order as input (each includes an icon URL) - total_codes: Number of codes processed

Map icon tip: Each result includes an icon field — a PNG URL for the weather condition. When building a GeoJSON FeatureCollection for a map, put each result's icon in the corresponding feature's properties.icon so the map shows weather icons instead of default blue pins.

Example: # After batch forecast returns codes for multiple cities: result = await batch_interpret_weather_codes("3,51,61,95") # Returns all interpretations in one call instead of 4 separate calls

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
weather_codesYes
Behavior5/5

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

No annotations provided, but description fully discloses behavior: input format (comma-separated ints 0-99), output structure (BatchWeatherCodeResponse with results list and total_codes), and icon details for map integration.

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?

Well-organized with sections (Args, Returns, Example) but contains slightly verbose map icon tip. Still efficient overall with no wasted sentences.

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?

With one parameter, no output schema, and no annotations, the description covers input format, output structure, use case, and example. Complete enough for effective tool invocation.

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

Parameters5/5

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

Single parameter weather_codes has 0% schema description coverage, but the description explains its format (comma-separated integers, 0-99), provides examples, and adds map icon usage tip – far beyond schema.

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?

Description starts with 'Interpret multiple WMO weather codes in a single call' – a specific verb+resource. It clearly distinguishes from sibling 'interpret_weather_code' by emphasizing batching.

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?

Explicitly tells when to use: 'instead of calling interpret_weather_code multiple times... pass all codes at once' and gives efficiency rationale for processing weather data for multiple locations.

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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