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Crawlora MCP

popeyes_menu

Read-only

Popeyes restaurant menu grouped into categories, with real per-store pricing merged in from RBI's ordering platform. Items also carry published nutrition facts and allergen data when Popeyes reports them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNoOptional. Restaurant market: US (default) or CA.
store_idYesRequired. Popeyes' numeric store id, from /popeyes/locations.

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

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description adds genuine behavioral context beyond that: pricing is real and per-store, sourced from RBI's ordering platform, and nutrition/allergen data is present only conditionally ("when Popeyes reports them"), warning the agent that fields may be sparse.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core resource and structure. Both sentences carry relevant information, though the nutrition/allergen clause could be tightened. No filler or redundant restatement of the name.

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 and annotations covering the safety profile, the description does not need to explain return values. It supplies the domain context (category grouping, per-store pricing, conditional nutrition data) an agent needs to call it correctly; only when-to-use guidance is missing.

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 market and store_id are fully documented in the schema (including the store_id source endpoint). The description's mention of "per-store pricing" loosely reinforces the store_id requirement but adds no syntax or format detail beyond the schema, matching the baseline 3.

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 states a specific resource (Popeyes restaurant menu) and the structure of what it returns (categories, per-store pricing, nutrition, allergens). An agent can identify it as the menu-retrieval tool. It does not explicitly name how it differs from siblings like popeyes_locations or popeyes_offers, so it falls just short of 5.

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?

There is no explicit guidance on when to use this tool versus alternatives (popeyes_locations, popeyes_offers, popeyes_promotions). It implies a store_id is needed to get pricing, but never states prerequisites or exclusions. The agent must infer the use case entirely.

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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