wait_time_trend

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

With no annotations provided, the description carries the full behavioral disclosure burden and does an excellent job. It discloses the output return fields (start/end values, change, percentage, direction, full monthly series), the units (weeks), and the found=false edge case, explicitly instructing not to invent a trend when data is absent.

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 compact yet information-dense: purpose, units, input guidance, output summary, and edge-case handling are all included in three sentences. Every sentence earns its place and no filler exists.

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 modest complexity, an output schema, and no annotations, the description is complete. It covers the data source, input semantics, units, expected return values, and the missing-data behavior, so an agent has everything needed to invoke it correctly and interpret results appropriately.

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%, so the description must compensate for both parameters. It does by explaining that provider_code is an NHS trust code with an example ('RGT') and specialty is a clinical specialty with an example ('Cardiology'), giving an agent enough semantic grounding to call the tool correctly.

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 a specific verb ('Summarise') and a specific resource ('waiting time for a given NHS trust and specialty... moved over time'), which distinguishes it from sibling tools like lookup_wait_time or rank_trusts_by_wait. It also previews concrete outputs, leaving no ambiguity about what the tool does.

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 clearly implies when to call this tool: when a trend summary over time is needed, rather than a point lookup or ranking. It gives concrete input requirements (provider code, specialty) and an explicit no-data behavior, but it does not explicitly name alternative tools or state when not to use it.

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