franchisedata-mcp

Hyperlocal Digital Shelf Share of Choice

get_local_share_of_choice

Read-onlyIdempotent

Diagnose local delivery channel share loss by querying DoorDash, Uber Eats, and first-party shelf telemetry for basket price gaps, ETA disparities, and sponsored placement.

Instructions

PAID ($0.005). Query local digital shelf telemetry from DoorDash, Uber Eats, and 1st-party apps, including delivered basket price gaps, delivery ETA disparities, and organic vs sponsored placement. Atomically debits $0.005. Requires 'fc_live_...' API key. Returns 402 if balance insufficient. WHEN TO USE: Use to diagnose delivery channel share loss, competitor promotion surges, and aggregator markup disparities. WHEN NOT TO USE: Do not use for static national FDD disclosures (use 'get_fdd_item19'). ALTERNATIVES: Use 'recommend_local_action' to generate guardrailed tactical responses based on shelf telemetry; use 'get_menu_pricing' for in-store menu pricing.

Input Schema

TableJSON Schema

Name	Required	Description
`daypart`	No	Optional daypart filter: 'lunch', 'dinner', or 'late_night'
`platform`	No	Optional delivery platform filter: 'doordash', 'ubereats', or 'first_party'
`store_id`	Yes	Target store ID (e.g. 'loc_mcdonalds_1001')

Output Schema

TableJSON Schema

Name	Required	Description
`store_id`	Yes	Target store ID
`rival_brand`	No	Top rival brand slug
`observations`	No	List of raw digital shelf observation records
`rival_promo_badge`	No	Active promotional badge of rival
`total_observations`	Yes	Total panel observations recorded
`balance_remaining_usd`	Yes	Remaining API credit balance in USD
`focal_delivered_total_usd`	No	Delivered basket total for focal store
`top_rival_delivered_total_usd`	No	Delivered basket total for top competitor

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv1.0.1Oct 6, 2026

changedInput schema / properties / daypart / description

Previous value: -"Optional filter: 'lunch', 'dinner'"New value: +"Optional daypart filter: 'lunch', 'dinner', or 'late_night'"

addedInput schema / properties / daypart / enum

Added value: +[
+  "lunch",
+  "dinner",
+  "late_night"
+]

changedInput schema / properties / platform / description

Previous value: -"Optional filter: 'doordash', 'ubereats', 'first_party'"New value: +"Optional delivery platform filter: 'doordash', 'ubereats', or 'first_party'"

addedInput schema / properties / platform / enum

Added value: +[
+  "doordash",
+  "ubereats",
+  "first_party"
+]

changedInput schema / properties / store_id / description

Previous value: -"Store ID (e.g. 'loc_mcdonalds_1001')"New value: +"Target store ID (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"
+    },
+    "focal_delivered_total_usd": {
+      "description": "Delivered basket total for focal store",
+      "type": "number"
+    },
+    "observations": {
+      "description": "List of raw digital shelf observation records",
+      "type": "array"
+    },
+    "rival_brand": {
+      "description": "Top rival brand slug",
+      "type": "string"
+    },
+    "rival_promo_badge": {
+      "description": "Active promotional badge of rival",
+      "type": "string"
+    },
+    "store_id": {
+      "description": "Target store ID",
+      "type": "string"
+    },
+    "top_rival_delivered_total_usd": {
+      "description": "Delivered basket total for top competitor",
+      "type": "number"
+    },
+    "total_observations": {
+      "description": "Total panel observations recorded",
+      "type": "integer"
+    }
+  },
+  "required": [
+    "store_id",
+    "total_observations",
+    "balance_remaining_usd"
+  ],
+  "type": "object"
+}

First observedv1.0.0Oct 1, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the read-only/idempotent annotations, it discloses cost ($0.005), atomic debit semantics, required 'fc_live_...' key format, and the 402 failure mode for insufficient balance. This is exactly the operational context annotations cannot carry.

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?

Front-loads cost and purpose, then structures use/avoid/alternatives into labeled clauses. Dense but every sentence carries routing or behavioral 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?

With an output schema present, the description need not explain return values, and it covers cost, auth, failure mode, and sibling routing. Nothing an agent needs to invoke 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 documented enums, so the baseline is 3. The description adds no parameter-level detail (no explanation of daypart/platform/store_id semantics) beyond naming the data domains.

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 and resource ('Query local digital shelf telemetry') and enumerates the concrete data returned: price gaps, ETA disparities, organic vs sponsored placement. It is clearly distinguishable from siblings like get_fdd_item19 or get_menu_pricing.

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?

Provides explicit WHEN TO USE (delivery channel share loss, competitor promotion surges, aggregator markup disparities), WHEN NOT TO USE (static national FDD disclosures -> get_fdd_item19), and ALTERNATIVES (recommend_local_action, get_menu_pricing). Routing is fully specified.

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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