get_meal_scan_count

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

With no annotations present, the description carries the full behavioral disclosure burden. It usefully discloses that the data is public, that no authentication is required, and that api_key is optional for consistency. However, it does not discuss behaviors like aggregation semantics, date-time boundary handling, or what happens when no scans exist, relying instead on the output schema for return details.

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 and well-structured, with a clear first-sentence summary followed by a brief Args block. Every line adds useful information, and there is no fluff or repetition.

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 is provided, so return-value details do not need to be repeated. The description covers all parameters, defaults, and the auth behavior. It could be slightly more complete by noting how aggregation treats no-scan or partial days, but the combination of parameter docs and output schema is adequate for a simple data-fetch tool.

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 fully document parameters. It does: meal is given an explicit enum-like list, mess has an example, date has a format and default, and api_key is flagged optional. This adds substantial meaning beyond the bare schema.

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 states a specific verb ('Return') and resource ('aggregated meal scan/availment counts for a mess on a given day'), making the tool's purpose clear. It does not explicitly contrast with sibling tools such as get_cancellation_count or get_capacities, so it falls just short of perfect differentiation.

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 implies usage context by naming the required inputs and noting that this is public data, but it does not provide explicit when-to-use/when-not-to-use guidance or alternatives. A user can infer the tool is for daily meal scan totals, but there is no direct routing to or away from sibling tools.

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