Weather report (pydantic-ai + sampling)

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

No annotations are provided, so the description carries the full behavioral burden, and it only partially fulfills it. It discloses the core transformation behavior but stays silent on the non-deterministic sampling implied by the title — outputs are LLM-generated and may vary between calls — as well as latency or possible failure modes. An agent invoking this tool has no warning that repeated calls with identical input may not produce identical forecasts.

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?

A single 19-word sentence with no filler; the core action is front-loaded. Minor quibble: the 'pydantic-ai agent' phrasing repeats implementation detail already present in the title, but nothing is bloated or redundant.

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?

The output schema exists, so return values need not be described. For a one-parameter tool with an output schema, the description covers the core contract, but it omits the sampling-driven behavior and sibling-usage guidance, so an agent must infer important operational context rather than have it stated.

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% and the description never mentions the location parameter, so the description adds nothing to parameter meaning. The schema self-documents reasonably via the enum ['Zurich', 'Savognin'] and default 'Zurich', which mitigates the gap, but the 0% coverage means the description was expected to compensate and did not.

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 names a specific transformation — 'turns the structured readings for a city into a short spoken-style forecast' — with a clear verb, resource, and output form. The contrast with the sibling get_structured_weather is implicit but recognizable: this tool synthesizes a spoken-style narrative rather than returning raw structured data. It is clear, though it never explicitly names the sibling it differs from.

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?

No explicit when-to-use or when-not-to-use guidance is given. The closest signal is the implicit contrast with get_structured_weather ('structured readings' vs 'spoken-style forecast'), which an agent must infer rather than be told. There are no exclusions or prerequisite conditions stated, leaving usage context implied rather than explicit.

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