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

Beyond the read-only/idempotent/non-destructive annotations, the description discloses key behavioral traits: the comfortable-day definition is a stated, temperate-biased preference rather than an objective measure, the data is reanalysis rather than station observations, and country queries return a named representative city. It also explains that mean maximum temperature and rainfall travel with each row, which is important context for callers who want alternative ranking criteria.

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 dense but not bloated; every sentence contributes substantive information. It front-loads the core purpose, then explains modes, then the comfort-day caveat and data source. It is a bit longer than strictly necessary, but the additional context about temperate bias and reanalysis data 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?

Given the tool's complexity, the rich output schema, and the read-only annotations, the description is complete. It covers data provenance, metric definitions, mode selection, country handling, and ranking behavior. Minor edge cases like what happens if both `city` and `country` are supplied are not addressed, but they are well-covered by the schema and not essential for 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 covers all five parameters at 100% coverage, providing a baseline of 3. The description adds meaningful semantic context beyond the schema: the distinction between returning a single place's months versus ranking cities, the representative-city behavior for `country`, the meaning of omitting `month`, and the subjective definition of comfortable days. This pushes the score above baseline.

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 identifies the tool's function: retrieving monthly climate normals and comfortable-day counts from ERA5/Open-Meteo. It distinguishes itself from sibling comparison tools by specifying the data source, metrics, and modes (city/country vs. ranked cities), so an agent understands what this tool uniquely offers.

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 explains when to pass `city` or `country` versus when cities will be ranked, and why a country resolves through a representative city. It also gives guidance on choosing month-level vs. annual ranking. It does not explicitly name alternative tools or state when not to use this tool, but the mode-based instructions are clear enough for most selection scenarios.

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