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shrey715

Mess MCP Server

list_available_extras

Discover which extra dishes, like omelettes or specials, are available for breakfast, lunch, snacks, or dinner on a chosen date at a specific mess.

Instructions

List extra items (e.g. omelettes, special dishes) available to book for a meal.

Args: meal_type: One of 'breakfast', 'lunch', 'snacks', 'dinner'. date: Target date (YYYY-MM-DD). Defaults to today. mess: Filter results to a specific mess. Optional. api_key: API key. Falls back to MESS_API_KEY environment variable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNo
messNo
api_keyNo
meal_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral transparency burden. It discloses useful details beyond the schema: date defaults to today, mess is an optional filter, and api_key falls back to the MESS_API_KEY environment variable. It does not explicitly state read-only behavior, but 'List' strongly implies it.

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 description is concise and well-structured: a front-loaded purpose sentence followed by a tight Args block. Every line adds information, and there is no filler or repetition.

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?

With an output schema present, the description does not need to explain return values. It covers all parameters, defaults, and auth fallback, which is enough for an agent to invoke the tool correctly. The only minor gap is not mentioning empty-result or error behavior, which is acceptable for a simple list query.

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 fully compensates by explaining every parameter: meal_type gets enumerated options, date gets format and default, mess gets its filtering purpose, and api_key gets its fallback behavior. This is exactly the semantic value the schema lacks.

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

Purpose4/5

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

The opening sentence names a specific operation ('List') and resource ('extra items ... available to book for a meal'), with concrete examples. It clearly conveys what the tool does, though it does not explicitly differentiate it from siblings like list_extras_in_range.

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

Usage Guidelines3/5

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

Usage is implied by the phrase 'available to book for a meal' and the meal_type/date arguments, but the description never states when to prefer this tool over alternatives such as list_registered_extras or list_extras_in_range. It also provides no explicit when-not-to-use guidance.

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