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yoyooyooo

Weather API MCP Server

by yoyooyooo

getWeatherForecast

Retrieve multi-day weather forecasts for any location using city names, coordinates, or zip codes. Configure temperature units, forecast duration, and language preferences to plan activities and prepare for upcoming conditions.

Instructions

Get weather forecast for a location. Returns weather forecast for the next few days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationNoThe location to get weather forecast for (city name, zip code, coordinates, etc.). If not provided, default location will be used.
optionsNoForecast configuration options, all fields are optional
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return type ('weather forecast for the next few days') but lacks critical details: whether this is a read-only operation, any rate limits, authentication requirements, error conditions, or what the 'default location' is. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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?

The description is appropriately concise with two sentences that directly state the tool's function and return value. There's no unnecessary fluff or repetition. However, it could be slightly more front-loaded by immediately clarifying the tool's scope relative to siblings.

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

Completeness3/5

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

Given the tool's moderate complexity (2 parameters, nested objects) and lack of both annotations and output schema, the description is minimally adequate. It explains what the tool does but misses key contextual elements: no sibling differentiation, no behavioral details beyond basic return type, and no output format explanation. It meets the bare minimum but has clear gaps.

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 schema fully documents both parameters ('location' and 'options') and their sub-properties. The description adds no parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('weather forecast for a location'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'getHourlyWeather' or 'getWeather', which likely provide different types of weather data. The description is specific about what it returns ('forecast for the next few days') but could better distinguish from alternatives.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus its siblings ('getHourlyWeather', 'getWeather', 'lookupCity'). It doesn't mention any prerequisites, exclusions, or alternative scenarios. The agent must infer usage from tool names alone, which is insufficient for optimal selection.

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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