franchisedata-mcp

Detect In-Store POS and Kitchen Tech Stack

detect_tech_stack

Read-onlyIdempotent

Identify POS hardware, kitchen display systems, and online ordering gateways for a store by brand and store ID. Use for competitive tech auditing and vendor market share tracking.

Instructions

PAID ($0.003). Fingerprint point-of-sale (POS) hardware (Toast, NCR Aloha, Brink, Micros, Square), kitchen display systems (KDS), and online ordering gateways for a store location. Atomically debits $0.003. Requires 'fc_live_...' API key. Returns 402 if balance insufficient. WHEN TO USE: Use for restaurant tech competitive auditing, vendor market share tracking, and POS displacement prospecting. WHEN NOT TO USE: Do not use for menu item prices (use 'get_menu_pricing') or store status (use 'starbucks_store_status'). ALTERNATIVES: Use 'get_menu_pricing' for item prices; use 'get_operator_intelligence' for franchisee ownership.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`brand`	Yes	Canonical brand slug (e.g. 'chickfila', 'sweetgreen', 'subway', 'mcdonalds')
`store_id`	Yes	Unique store number or store ID (e.g. 'loc_subway_1001')

Output Schema

TableJSON Schema

Name	Required	Description	Default
`tech_stack`	No	Fingerprinted technology components for the location
`balance_remaining_usd`	Yes	Remaining API credit balance in USD

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv1.0.1Oct 6, 2026

changedInput schema / properties / brand / description

Previous value: -"Brand slug"New value: +"Canonical brand slug (e.g. 'chickfila', 'sweetgreen', 'subway', 'mcdonalds')"

changedInput schema / properties / store_id / description

Previous value: -"Store ID or store number"New value: +"Unique store number or store ID (e.g. 'loc_subway_1001')"

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "balance_remaining_usd": {
+      "description": "Remaining API credit balance in USD",
+      "type": "string"
+    },
+    "tech_stack": {
+      "description": "Fingerprinted technology components for the location",
+      "properties": {
+        "brand_id": {
+          "type": "string"
+        },
+        "hardware_terminal_model": {
+          "type": "string"
+        },
+        "kds_system": {
+          "type": "string"
+        },
+        "online_ordering_gateway": {
+          "type": "string"
+        },
+        "pos_confidence": {
+          "type": "number"
+        },
+        "pos_provider": {
+          "type": "string"
+        },
+        "store_id": {
+          "type": "string"
+        }
+      },
+      "type": "object"
+    }
+  },
+  "required": [
+    "balance_remaining_usd"
+  ],
+  "type": "object"
+}

First observedv1.0.0Oct 1, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations only declare readOnly/idempotent/closed-world; the description adds the non-obvious operational facts an agent needs: a $0.003 paid call, atomic debit semantics, the required 'fc_live_...' key prefix, and a 402 response when balance is insufficient. This is real value beyond the structured fields, and the billing behavior does not contradict the readOnly annotation.

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-loaded with cost and capability, then constraints, then routing — a sensible priority order with no filler sentences. The brand enumeration and ALL-CAPS section labels are dense but each clause carries distinct information.

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?

An output schema exists, so return-shape explanation is unnecessary; the description fills the remaining gaps that structured fields do not (price, billing atomicity, auth key, 402 failure mode, usage routing). An agent has everything needed to call this correctly or route elsewhere.

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 examples for both brand and store_id, so the schema already carries the parameter burden. The description adds no format, canonicalization, or validation detail beyond what the schema states, so the baseline 3 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?

States a specific verb (fingerprint) plus the exact resources targeted (POS hardware brands, KDS, online ordering gateways) and scopes it to a single store location. It explicitly names the sibling tools it is not (get_menu_pricing, starbucks_store_status), so an agent can disambiguate 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?

Explicitly structured as WHEN TO USE (tech competitive auditing, vendor share tracking, POS displacement prospecting), WHEN NOT TO USE with the correct alternatives named, and an ALTERNATIVES line routing get_menu_pricing and get_operator_intelligence to their proper cases. 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.

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