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

With no annotations provided, the description carries the full disclosure burden. It does state the output type ('time in minutes') and the data source ('national timetable'), but omits any edge-case behavior such as how departure time is handled, accuracy, or error conditions. This is a minimum viable disclosure for a read-only lookup.

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 a single, information-dense sentence with no filler. It front-loads the core return value and efficiently conveys mode, scope, and data source.

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 simple two-parameter, read-only tool without an output schema, this description is complete: an agent knows exactly what result to expect (minutes), the input restrictions (place/station names), and the scope (Swiss public transport). No critical information is 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?

The input schema already provides 100% parameter coverage, describing both 'from' and 'to' as place/station names and noting the default value for 'to'. The description adds no additional parameter-specific meaning, so it does not need to compensate; the baseline 3 applies.

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 with a specific verb ('returns') and resource ('public-transport journey time in minutes between two Swiss places'), adding the data source ('national timetable'). This strongly differentiates it from the sibling commute_cost, which presumably addresses cost rather than time.

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 about when to use the tool: for public-transport travel times in Switzerland. While it does not explicitly name alternatives or exclusion conditions, the precise scope makes the appropriate use case unambiguous relative to the listed sibling tools.

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