purdue-mcp

Live dining court line lengths

dining_line_length

Get real-time line lengths at Purdue dining courts to avoid long waits. Uses live crowd-sourced data.

Instructions

Crowdsourced real-time line-length reports for Purdue dining courts. Often empty outside peak hours. Source: Purdue HFS dining API (live).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "dining_line_length"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Aug 18, 2026

TDQS

A3.7/5.0

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 discloses the source (live API, crowdsourced) and implies the data is real-time and may be 'empty' at off-peak times. While this adds transparency, it does not state behaviors like freshness interval, what happens if no reports exist, or any rate limits. It is adequate but not comprehensive for a zeroparam tool.

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 two sentences, front-loads the core purpose, and includes only relevant additional context (off-peak emptiness, source). Every sentence earns its place with no wasted words. Excellent conciseness.

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 has no parameters, no output schema, and no annotations, the description provides sufficient context for a simple live data retrieval: what it returns, its source, and a usage hint. It lacks explicit mention of data format or expected output structure, but for a zero-param, read-only style tool, this is mostly complete.

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

Parameters4/5

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

The tool has zero parameters and schema coverage is 100% (empty properties). The description adds value by explaining what data the tool returns (line-length reports for dining courts) and its nature (crowdsourced, live). For a parameterless tool, this is above baseline; the description meaningfully sets expectations.

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 tool provides 'real-time line-length reports for Purdue dining courts' and identifies the data source (Purdue HFS dining API). The verb 'Crowdsourced real-time line-length reports' effectively communicates the purpose and distinguishes it from sibling tools like dining_menu or dining_locations, though it could be more explicit about it being a live retrieval, not historical analysis.

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?

The description notes 'Often empty outside peak hours', giving implicit guidance on when the tool returns value vs. less relevant data. However, it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among the many sibling tools for dining or campus info. Usage context is hinted but not fully clarified.

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