Weather forecast for a Spanish municipality

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

With no annotations, the description carries the full burden of behavioral disclosure. It explains that the tool returns forecast data, describes the daily fields (temperature min/max, sky state, precipitation probability, wind), and states the hourly horizon (~40h). It does not describe error cases or response structure, but for a read-only forecast lookup the covered behavior is substantial.

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 with zero filler. It front-loads the core purpose, then gives input formats, then output details. Every sentence earns its place.

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 tool with only three parameters and no output schema, the description covers what an agent needs to call it correctly: accepted location identifiers, the meaning of both granularities, the days range context, and the kind of response data to expect. There is no critical missing operational context that would prevent correct invocation.

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 already documents all three parameters at 100% coverage, so the baseline is 3. The description adds extra meaning by noting that names work with or without accents and by explaining what daily vs hourly granularity actually returns, which goes beyond the schema's parameter descriptions.

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 opens with a specific verb and resource: 'Returns the AEMET forecast for any of Spain's 8000+ municipalities.' It clearly distinguishes this from sibling tools by emphasizing municipality-level general forecasts and by listing both daily and hourly granularities, so an agent can tell it apart from beach, mountain, maritime, or historical tools.

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 gives clear context for when this tool is appropriate: whenever a general municipal weather forecast for Spain is needed, by name or INE code. It does not explicitly name alternatives or state when not to use it, but the municipal scope and granularity options make the intended usage unambiguous.

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