segment_journey

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

No annotations are provided, so description carries the burden. It discloses the input formats (CSV or JSON), the segmentation rule, and the output structure (segments with number, start, end, duration, sample count, temperature stats). It doesn't mention edge cases like single-sample segments, how the first/last segment boundary is handled, or data quality behavior for missing timestamps.

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 reasonably concise with front-loaded purpose, followed by context, parameter docs, and return format. No wasted sentences. The Polish language is consistent throughout, and the structure (purpose, context, args, returns) is logical and scannable.

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?

An output schema exists, so return values don't need to be described in detail—yet the description still provides a useful summary of the output fields. No annotations, so the description reasonably covers the tool's behavior. For a 4-parameter tool with CSV/JSON input flexibility, the description is adequate, though it could mention error conditions or how the timestamp field is parsed.

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 compensates partially by explaining each parameter in Polish: data (CSV/JSON), gap_minutes (the time gap threshold), ts_field and value_field (column names). The names and defaults in the schema are fairly self-explanatory, and the description adds modest context about their roles but doesn't cover defaults meaning or edge cases for malformed data.

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 verb+resource action: 'Dzieli ciągły log na osobne przejazdy' (divides continuous log into separate journeys) based on time gaps. It distinguishes this from siblings (which focus on stats, threshold detection, and anomaly detection) by specifying the segmentation logic. However, it doesn't explicitly contrast with sibling 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 explains when to use this tool: when a transport logger records multiple journeys in a single file and you need to split them. It clearly defines the triggering condition (gap longer than gap_minutes marks a boundary). It could be improved by mentioning what it's NOT for (e.g., not for computing summary stats or detecting anomalies, which siblings handle).

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