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purdue-mcp

purdue-mcp

by sharziki
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Find a food item across dining courts

dining_find_item

Search all Purdue dining court menus for a specific dish on a given date. Instantly find which location serves your desired meal, including retail options.

Instructions

Search every Purdue dining court's menu for a dish on a given date — answers 'who is serving chicken tenders today'. Source: Purdue HFS dining API (live).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "dining_find_item"
  }
}

Input Schema

NameRequiredDescriptionDefault
dateNoYYYY-MM-DD. Defaults to today on campus.
mealNoRestrict to a meal period, e.g. 'Dinner'.
queryYesDish or keyword, e.g. 'chicken tenders', 'pho', 'cookie'.
include_retailNoAlso search Quick Bites / On-the-GO! locations. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of disclosing behavior. It adds useful context: the tool sources live data from the Purdue HFS dining API and answers the 'who is serving' question. However, it does not disclose return format, no-match behavior, or whether retail locations are included by default (though the schema hints at include_retail).

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 one concise sentence with a clear main clause, a defining example, and a source attribution. Every element serves a purpose and there is no unnecessary repetition or filler.

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 simple search tool with no output schema, the description adequately conveys the tool's function, cross-court scope, date sensitivity, and live data source. It does not describe the response structure or edge cases, but the core selection and invocation context is clear.

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?

The input schema provides 100% coverage with meaningful descriptions for all four parameters, including examples and defaults. The description only repeats the query idea ('chicken tenders') and date concept ('on a given date'), adding no additional semantic value beyond what the schema already offers. Baseline 3 is appropriate.

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 a specific verb ('Search') and clearly defines the resource ('every Purdue dining court's menu') and scope ('for a dish on a given date'). The example 'who is serving chicken tenders today' makes the tool's purpose unmistakable and differentiates it from siblings like dining_menu (which likely shows a single court's menu).

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 gives a clear use case: finding which dining court serves a specific dish on a given date. It implies cross-court lookup rather than per-court menu browsing, which helps disambiguate from dining_menu. However, it does not explicitly state when not to use the tool or name alternative tools.

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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