Weather MCP

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

Annotations already indicate readOnlyHint=true, so no further safety disclosure is needed. The description adds behavioral context beyond the annotation by detailing the response contents (per-day high/low, precipitation chance, max wind, conditions) and the forecast range, which helps the agent set expectations without needing to inspect the output schema.

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

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

The description is compact and well-structured: it starts with the core purpose and output, then gives usage guidance with examples, and ends with a parameter clarification. Every sentence contributes—no filler, repeated schema information, or unnecessary detail.

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

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

Given the simple two-parameter schema and the presence of an output schema, the description is complete. It explains what the tool returns, when to use it, how to interpret the 'days' parameter, and the default behavior. There are no significant gaps for an agent to invoke this tool correctly.

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

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

Schema description coverage is 0%, but the description compensates well for the 'days' parameter by specifying 'how many days ahead (1-7, default 5)'. The 'city' parameter is not explicitly defined, but the examples ('Paris', 'Bangkok') and the phrase 'for a city' make its meaning immediately clear, so the description adds sufficient semantic value beyond the raw schema.

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

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

The description clearly states this is a 'Daily weather forecast for a city' with a defined scope ('up to 7 days ahead') and specific output fields (high/low, precipitation chance, wind, conditions). It distinguishes itself from sibling 'get_weather' by emphasizing forward-looking, future weather questions rather than current conditions.

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

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

The description explicitly says 'Use for forward-looking questions' and provides concrete examples ('will it rain in Paris this weekend?', 'what's the weather in Bangkok next week?', packing/trip-planning). It does not name the alternative 'get_weather' or state when not to use it, but the guidance clearly implies a contrast with current-weather use.

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

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

Annotations only mark readOnlyHint=true. The description adds valuable behavioral context: the exact output fields (temperature in Celsius, humidity, wind speed, plain-language description), the input flexibility (any language, optional country for disambiguation), and the scoping to current conditions. No contradiction with annotations.

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

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

The description is three sentences, tightly packed. It opens with the core purpose and output, then gives usage examples, then ends with input guidance. Every sentence adds value, with no redundancy or filler.

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

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

For a single-parameter tool with an output schema and a read-only annotation, the description is fully adequate. It covers what the tool returns, when to use it, and how to specify the input. The presence of an output schema relieves the description from detailing return structure, and no gaps are apparent.

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

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

The input schema only says 'city' is a string with no description. The description compensates by explaining the city parameter: accepts any language, and country can be added for ambiguous names (e.g., 'Springfield, US'). This adds meaning beyond the bare schema definition.

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

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

The description clearly states the tool's function: 'Current weather conditions for a city, right now.' It lists specific fields (temperature, feels-like, humidity, wind speed) and distinguishes itself from the sibling get_forecast by emphasizing the 'right now' / 'at this moment' scope.

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

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

The description explicitly says 'Use for any question about the weather at this moment' and provides concrete example queries. It does not explicitly say 'do not use for forecasts' or name the alternative tool, but the 'right now' language and sibling tool name make the boundary clear.

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