Skip to main content
Glama

Retrieve Store Menu Pricing and Price Variance

get_menu_pricing
Read-onlyIdempotent

Extract localized menu item prices for franchise restaurants, benchmark variances against national averages, and detect delivery aggregator markups to support pricing audits.

Instructions

PAID ($0.003). Extract localized menu item prices, benchmark variances against national averages, and identify delivery aggregator price markups. Atomically debits $0.003. Requires 'fc_live_...' API key. Returns 402 if balance insufficient. WHEN TO USE: Use for local pricing strategy audits, inflation index tracking, and menu margin variance analysis. WHEN NOT TO USE: Do not use for delivery shelf placement rankings (use 'get_local_share_of_choice'). ALTERNATIVES: Use 'get_local_share_of_choice' for DoorDash/UberEats basket comparisons; use 'get_fdd_item19' for national revenue benchmarks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandYesCanonical brand slug (e.g. 'mcdonalds', 'chipotle', 'subway')
store_idYesStore ID or store number to query localized prices for (e.g. 'loc_mcdonalds_1001')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandYesBrand slug
itemsYesList of localized menu items with pricing and national variance
store_idYesStore identifier
balance_remaining_usdYesRemaining API credit balance in USD

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.0.1
    • changedInput schema / properties / brand / description
      Previous value: -"Brand slug"New value: +"Canonical brand slug (e.g. 'mcdonalds', 'chipotle', 'subway')"
    • changedInput schema / properties / store_id / description
      Previous value: -"Store ID or store number"New value: +"Store ID or store number to query localized prices for (e.g. 'loc_mcdonalds_1001')"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "balance_remaining_usd": {
      +      "description": "Remaining API credit balance in USD",
      +      "type": "string"
      +    },
      +    "brand": {
      +      "description": "Brand slug",
      +      "type": "string"
      +    },
      +    "items": {
      +      "description": "List of localized menu items with pricing and national variance",
      +      "items": {
      +        "properties": {
      +          "category": {
      +            "type": "string"
      +          },
      +          "item_name": {
      +            "type": "string"
      +          },
      +          "national_baseline_usd": {
      +            "type": "number"
      +          },
      +          "price_usd": {
      +            "type": "number"
      +          },
      +          "variance_pct": {
      +            "type": "number"
      +          }
      +        },
      +        "type": "object"
      +      },
      +      "type": "array"
      +    },
      +    "store_id": {
      +      "description": "Store identifier",
      +      "type": "string"
      +    }
      +  },
      +  "required": [
      +    "store_id",
      +    "brand",
      +    "items",
      +    "balance_remaining_usd"
      +  ],
      +  "type": "object"
      +}
  2. First observedv1.0.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations cover the safety profile (readOnlyHint, idempotentHint, openWorldHint=false), and the description adds substantial context beyond them: a $0.003 cost, atomic debit semantics, the required 'fc_live_...' API key format, and the 402 insufficient-balance failure mode. This is exactly the billing/auth behavior an agent needs and cannot get from annotations.

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?

Front-loads the paid cost and core capability, then usage, then routing, all in a compact block. Slight redundancy in stating the $0.003 price twice ('PAID ($0.003)' and 'Atomically debits $0.003'), which keeps it just short of a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-param, paid, read-only lookup with an output schema present, the description covers cost, auth, failure mode, and routing without needing to describe return values. Nothing an agent needs to call it correctly 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% with only two parameters, so the schema already documents 'brand' (with slug examples) and 'store_id' (with example ID). The description adds no parameter-level syntax or format detail beyond that. Baseline 3 is appropriate when the schema does the heavy lifting.

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?

States specific verbs and resources — extract localized menu prices, benchmark variances, identify delivery markups — and names the exact siblings it is not (get_local_share_of_choice, get_fdd_item19). An agent can distinguish it from every 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit WHEN TO USE (pricing audits, inflation tracking, margin variance), WHEN NOT TO USE with the alternative named for the excluded case, and an ALTERNATIVES line routing to two specific sibling tools by use case. Nothing is left to inference.

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