purdue-mcp

Nutrition facts for a dining item

dining_item_nutrition

Retrieve the full nutrition panel for any Purdue dining menu item by its item ID from the live HFS dining API.

Instructions

Full nutrition panel for one menu item, by the item id returned from dining_menu or dining_find_item. 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": {
      "item_id": "<item_id>"
    },
    "name": "dining_item_nutrition"
  }
}

Input Schema

Name	Required	Description	Default
`item_id`	Yes	Item GUID from dining_menu / dining_find_item.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Aug 18, 2026

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 full burden. It identifies the tool as a read operation ('returns... panel', live API source) but does not explicitly state non-destructive behavior, error handling, or response limitations. For a simple read tool, this is adequate but could be more explicit.

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 front-loaded sentence plus a brief source note. Every part is necessary and no word is wasted. It is optimally concise.

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?

Given the tool's simplicity (one parameter, no output schema), the description adequately covers the input source and purpose. It could mention the expected output format (e.g., JSON object with nutrient fields), but the term 'full nutrition panel' provides sufficient guidance for an AI agent.

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 coverage is 100% and the parameter description already explains 'Item GUID from dining_menu / dining_find_item'. The tool description repeats this information without adding new semantics, meeting the baseline for high coverage.

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 clearly states the tool returns a 'full nutrition panel for one menu item', using an item ID from sibling tools. It distinguishes from dining_menu and dining_find_item which retrieve item lists rather than nutrition data. The verb 'returns' is implied but the purpose is unambiguous.

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 tells the agent to obtain the item ID from dining_menu or dining_find_item before using this tool, providing clear context. However, it does not include explicit when-not-to-use guidance or mention alternatives, though no competing nutrition tools exist among siblings.

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