franchisedata-mcp

Search Franchise Brands Catalog

search_brands

Read-onlyIdempotent

Search indexed US restaurant franchise brands by name, concept, sector, or category to find matching chains, unit counts, and category data. Use it to discover brand slugs or verify catalog coverage.

Instructions

FREE. Search and discover indexed restaurant brands across the 500 US franchise database by name, concept, sector, or category. Returns matching chains with total unit counts, category classification, and per-call endpoint pricing. Read-only operation requiring no API key or balance. Rate limit: 60 req/min. WHEN TO USE: Use first when discovering brand slugs or verifying if a chain is indexed in the catalog. WHEN NOT TO USE: Do not use for physical store locations (use 'franchise_search_near' or 'mcdonalds_locations'), corporate filings (use 'get_fdd_item19'), or natural language vector searches (use 'semantic_vector_search'). ALTERNATIVES: Use 'semantic_vector_search' for conceptual similarity; use 'describe_endpoint' to inspect route pricing.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	No	Case-insensitive search query matching brand name, category, or slug (e.g. 'burger', 'coffee', 'taco', 'subway'). If omitted, returns top brands ordered by fleet size.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`total`	Yes	Total matching brands found
`brands`	Yes	List of matching brand summaries with fleet size and API prices

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.0.1Oct 6, 2026

changedInput schema / properties / query / description

Previous value: -"Optional search query (e.g. 'burger', 'coffee', 'pizza')"New value: +"Case-insensitive search query matching brand name, category, or slug (e.g. 'burger', 'coffee', 'taco', 'subway'). If omitted, returns top brands ordered by fleet size."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "brands": {
+      "description": "List of matching brand summaries with fleet size and API prices",
+      "items": {
+        "properties": {
+          "category": {
+            "type": "string"
+          },
+          "id": {
+            "type": "string"
+          },
+          "name": {
+            "type": "string"
+          },
+          "price_per_call": {
+            "type": "number"
+          },
+          "slug": {
+            "type": "string"
+          },
+          "tagline": {
+            "type": "string"
+          },
+          "total_locations": {
+            "type": "integer"
+          },
+          "website": {
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total": {
+      "description": "Total matching brands found",
+      "type": "integer"
+    }
+  },
+  "required": [
+    "total",
+    "brands"
+  ],
+  "type": "object"
+}

First observedv1.0.0Oct 1, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, but the description adds cost ('FREE'), auth requirements ('no API key or balance'), and a concrete rate limit (60 req/min). That is exactly the extra behavioral context annotations cannot convey.

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-loaded with the essential verb, resource and cost, then cleanly partitioned into WHEN TO USE / WHEN NOT TO USE / ALTERNATIVES. Dense but every clause 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?

An output schema exists, so return values need not be explained; the description nonetheless sketches the return shape (unit counts, category, pricing). Combined with the sibling routing and rate-limit info, nothing an agent needs to call this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds value by naming the queryable facets (name, concept, sector, category) and the default behavior when query is omitted, though 'concept, sector' drifts slightly from the schema's 'name, category, slug' wording.

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 ('Search and discover') and resource ('indexed restaurant brands across the 500 US franchise database'), plus the searchable dimensions. An agent can immediately distinguish it from siblings like semantic_vector_search or franchise_search_near.

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 (discovering brand slugs, verifying catalog indexation), WHEN NOT TO USE with named alternatives for each excluded case (franchise_search_near, mcdonalds_locations, get_fdd_item19, semantic_vector_search), and an ALTERNATIVES line. This is the gold standard for routing.

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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