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generate_local_business_schema

Generate LocalBusiness JSON-LD structured data with postal address, name, and contact details. Produce valid schema markup for local SEO to improve search result appearance.

Instructions

Generate LocalBusiness JSON-LD with a postal address.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "street_address": "<street_address>",
      "city": "<city>",
      "region": "<region>",
      "postal_code": "<postal_code>",
      "country": "<country>"
    },
    "name": "generate_local_business_schema"
  }
}

Input Schema

Name	Required	Description	Default
`url`	No
`city`	Yes
`name`	Yes
`region`	Yes
`country`	Yes
`telephone`	No
`postal_code`	Yes
`price_range`	No
`street_address`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

C2.8/5.0

Behavior2/5

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It mentions generation but does not describe the output format, whether the result is a string or object, side effects, or any constraints beyond the address fields. This is a thin description for a tool with no behavioral 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?

The description is a single, concise sentence with no fluff or redundancy. It is front-loaded and easy to read. However, given the tool's complexity, the brevity borders on under-specification, though conciseness itself is well handled.

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

Completeness2/5

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

For a tool with 9 parameters, no annotations, and a meaningful output (JSON-LD), the description is incomplete. It does not address what the output looks like, how the required address fields are used, or how it relates to sibling schema generators. The output schema exists but is not described, leaving the agent without essential context.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not compensate by explaining any parameter meanings, required fields, or defaults. While some field names are self-explanatory, the description adds no value beyond the raw schema property names, leaving 9 parameters with no semantic guidance.

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?

The description clearly states the tool's function: generating LocalBusiness JSON-LD, and specifies that it includes a postal address. This differentiates it from sibling schema generators like generate_article_schema, generate_faq_schema, and generate_breadcrumb_schema, making the purpose unambiguous.

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?

The description provides no guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. It simply states what it does, leaving the user to infer usage context from the tool name and siblings.

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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