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chrischall

myhotlunchbox-mcp

by chrischall

mhlb_get_day

Read-only

Retrieve a student's complete lunch order for a specified date, including item details, sizes, quantities, add-ons, and prices.

Instructions

Get what a student has ordered on one specific date — the items, sizes, quantities, add-ons and prices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesThe day to look at (YYYY-MM-DD).
studentIdYesStudent id from mhlb_list_students.
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating a safe, read-only operation with potentially non-enumerable results. The description does not contradict these annotations and adds a small amount of context by specifying the returned fields (items, sizes, quantities, etc.), but it does not disclose additional behavioral traits such as handling of missing dates, response format, or limits. Given the annotation coverage, a baseline 3 is appropriate.

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 a single, well-crafted sentence that front-loads the core action and resource, then immediately specifies the returned details. There is no filler or repetition. Every part of the sentence contributes to the agent's understanding. It is efficient and appropriately sized for the tool's simplicity.

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 read-only query tool with two parameters, a full schema, and annotations covering safety, the description provides sufficient context by enumerating the returned data fields. It does not mention edge cases like 'no order on that date' or whether the result is a single object or list, but given the openWorldHint and typical usage, these are minor omissions. The description is reasonably complete for an agent to invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so both parameters (studentId and date) are already documented in the schema with descriptions. The tool description adds no extra semantic information about the parameters—it only implies a date-based query. With full schema coverage, the description does not need to compensate, so the baseline 3 is accurate.

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 description clearly states the verb 'Get' and the resource 'what a student has ordered on one specific date', and enumerates the returned details (items, sizes, quantities, add-ons, prices). It is specific and unambiguous, though it does not explicitly differentiate from sibling tools like mhlb_get_order or mhlb_get_cart. However, the scope (a specific date) is a distinguishing feature, so it is clear enough for an agent.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, conditions, or situations where this tool is preferred over mhlb_get_order or other retrieval tools. An agent would have to infer usage solely from the resource name and schema, which is insufficient for nuanced decisions.

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

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