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shrey715

Mess MCP Server

submit_feedback

Rate a consumed meal with a star rating and optional comments for a specified date and meal type to provide structured quality feedback for mess dining.

Instructions

Submit quality feedback for a consumed meal.

Args: meal_date: Date the meal was consumed (YYYY-MM-DD). meal_type: One of 'breakfast', 'lunch', 'snacks', 'dinner'. rating: Star rating out of 5 (1 = poor, 5 = excellent). remarks: Optional free-text comment on taste, hygiene, temperature, etc. api_key: API key. Falls back to MESS_API_KEY environment variable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ratingYes
api_keyNo
remarksNo
meal_dateYes
meal_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.4/5.0
Behavior3/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It discloses the API key fallback and implies a side-effectful submission, but it does not describe result handling, validation behavior, idempotency, or failures. This is a moderate partial disclosure.

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 Args block is compact and scannable; each parameter receives exactly one line of meaningful documentation. There is no filler or redundant restating of the schema.

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

Completeness5/5

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

With no annotations and an output schema already available, the description covers all input semantics needed to call the tool correctly. It provides enum-like meal_type guidance, date formatting, rating semantics, optional fields, and auth fallback—complete for a straightforward submission tool.

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 supply meaning for all five parameters. It fully does: date format, allowed meal_type values, rating scale semantics, optional remarks, and API key fallback.

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 uses the explicit verb 'Submit', names the object 'quality feedback', and scopes it to a 'consumed meal'. Among all sibling tools, none handles feedback, so the purpose is unambiguously distinguished from the rest.

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 phrase 'for a consumed meal' gives clear context for when the tool should be used—after a meal has been consumed. It doesn't explicitly name alternatives or state when not to use it, but the context is clear enough relative to the sibling meal-management tools.

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