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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: station coverage (~300 stations, with ~140 being precipitation-only), data update frequency (every 10 minutes), the limitation that precipitation-only stations return only rainfall, and that 8 specific stations also include daily visual observations. It also mentions automatic nearest-station matching. This goes well beyond a minimal description, though it could still detail error handling or output format, but the output schema covers the latter.

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 concise and well-structured, with three short paragraphs: general data, special observations, and input methods. Every sentence adds value and no redundant information exists. It is front-loaded with the core purpose and quickly covers important details.

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 two optional parameters, no annotations, and the presence of an output schema, the description is remarkably complete. It explains what data is returned, the update interval, station types and limitations, special observational additions, and all accepted input formats. Nothing critical for invocation appears missing.

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 coverage is 100%, so baseline is 3. The description adds meaningful semantics beyond the schema: it explains that station accepts names, abbreviations, and addresses (with examples), and that coordinates are an alternative. It also adds the key behavior of automatically finding the nearest station, which is not in the schema. This enhances understanding of both parameters.

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 provides 'real-time weather measurements' from Swiss automatic weather stations, listing specific data types and station counts. This distinctively separates it from sibling tools like meteoswissLocalForecast (forecast), meteoswissClimateData (historical), and meteoswissStations (station metadata). The verb 'Get' and specific resource are explicit.

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 to use this tool: for current, real-time observations updated every 10 minutes. It also details input methods (station names, abbreviations, addresses, coordinates) and automatic nearest-station selection. However, it does not explicitly mention alternatives or state 'when not to use', so it lacks explicit exclusions that would earn a 5.

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