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Glama

rate_meal

Submit a 1-5 rating for a school meal's quality and quantity in EduPage, optionally for a specific date, meal type, or menu, to record feedback and update rating averages.

Instructions

Rate a school meal (quality + quantity, each 1-5). ⚠️ Writes a real rating into EduPage for the logged-in boarder/child.

Args: quality: Food quality score 1-5 (required) quantity: Portion quantity score 1-5 (required) date_str: Date in YYYY-MM-DD format. Leave empty for today. meal_type: snack / lunch / afternoon_snack (or 1/2/3). Default lunch. menu_number: Optional choosable menu id (1=A, 2=B, …). Empty = ordered/first menu. school: School subdomain (only needed with multiple schools).

Returns: JSON with ok=true, submitted scores, and refreshed rating averages

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
schoolNo
qualityYes
date_strNo
quantityYes
meal_typeNolunch
menu_numberNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly warns 'Writes a real rating into EduPage,' which is the critical side effect an agent must know before invoking a mutation tool. It stops short of full transparency about overwriting existing ratings or authentication requirements, hence 4 rather than 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The warning is front-loaded, then each parameter is a compact bullet with type/domain/default, followed by a one-line Returns section. Every sentence earns its place and the structure is easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with six parametersais and no annotations, the description covers the action, side effect, parameter meanings, defaults, and return shape. It is highly complete, though it could briefly note prerequisites like prior login or handling of repeat submissions; the output schema likely covers the return contract.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must document the parameters itself. It does so thoroughly for all six parameters, including required status, value ranges, date format, meal_type options/default, menu_number mapping, and the conditional nature of school.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with the specific action 'Rate a school meal' and defines its two scoring dimensions (quality and quantity, each 1-5). It is immediately distinguishable from sibling tools like get_meals or send_message.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: this writes a real rating for the logged-in boarder/child)Skip with a warning. It also explains defaults such as today's date and the lunch meal type. It does not explicitly name alternatives or when-not-to-use conditions, but no sibling appears to perform the same action.

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