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Crawlora MCP

fiveguys_menu

Read-only

Five Guys menu categories (each with its own short description and cover image) and items: name, description, calories (single value or a range), and image. No per-item price -- pricing is store-scoped and lives only behind a separate, credential-gated ordering flow not covered here.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoOptional. Content language, en (default) or es.
categoryNoOptional. One category slug to return only that category's items. Omit to return the full menu across every category. With lang=en (the default) one of: burgers, fries, shakes, dogs, sandwiches, drinks, toppings. With lang=es the slugs differ -- read them from a lang=es response's categories[].slug.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover safety (readOnlyHint) and openness (openWorldHint). Beyond that, the description adds meaningful behavioral context: prices are deliberately absent because they are store-scoped, and calories may be a range rather than a single value. This preempts an agent incorrectly expecting price data, though it doesn't describe pagination or localization behavior.

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?

Two tightly written sentences, front-loaded with what the tool returns before the notable exclusion. No filler and every clause carries information.

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?

With an output schema present, return values need not be re-explained, and the description covers both the content shape and the price exclusion. It omits any mention of the two-language behavior, which the schema handles, so it is essentially complete but not maximally so.

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 both parameters are thoroughly documented in the schema, including the language-dependent category slugs. The description adds nothing about lang or category, so the baseline 3 for a fully documented schema applies.

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 states a specific resource (Five Guys menu categories and items) and enumerates the exact fields returned (name, description, calories as single value or range, image). It also carves out scope by stating no per-item price and pointing to the separate credential-gated ordering flow, which lets an agent distinguish it from the ordering-menu sibling without opening a schema.

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?

It gives a clear implicit context (browse the general menu structure/content) and explicitly rules out the price/ordering use case, noting pricing 'lives only behind a separate, credential-gated ordering flow not covered here.' It stops short of naming that tool directly, so routing is inferred rather than explicit.

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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