AutoRank MCP

generate_article_schema

Create Article JSON-LD markup for search engines, specifying headline, author, and publication date to enhance rich results.

Instructions

Generate Article JSON-LD. Dates should be ISO 8601 (YYYY-MM-DD).

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": {
      "headline": "<headline>",
      "author_name": "<author_name>",
      "date_published": "<date_published>"
    },
    "name": "generate_article_schema"
  }
}

Input Schema

Name	Required	Description	Default
`headline`	Yes
`image_url`	No
`author_name`	Yes
`description`	No
`date_modified`	No
`date_published`	Yes
`publisher_name`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of disclosing behavior. It adds only one behavioral detail (dates must be ISO 8601), but it does not describe output format, whether the generated JSON-LD is validated, or any side effects. This is minimal for a generation tool.

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 composed of two short sentences with no redundant words. Each sentence adds some information: the first defines the action and resource, the second clarifies date format. However, the extreme brevity borders on under-specification, slightly reducing the score.

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 7 parameters, no annotations, and 8 siblings, this description is incomplete. It lacks usage context, parameter clarification, and sibling differentiation. While an output schema exists, the description still needs to explain when and how to use the tool effectively, which it does not.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for the 7 undocumented parameters. It adds meaning only to date-related parameters (date_published, date_modified) by specifying ISO 8601 format, but leaves headline, author_name, image_url, description, and publisher_name without any semantic guidance beyond their schema names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Generate' and the resource 'Article JSON-LD', identifying the tool's primary function. However, it does not explicitly differentiate from sibling schema generators like generate_faq_schema or generate_breadcrumb_schema, though the specific 'Article' type provides implicit distinction.

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?

There is no guidance on when to use this tool versus alternatives. It fails to mention that this is for article pages or that it should not be used for other schema types. No context about appropriate use cases or exclusions is provided.

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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