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sandipprajapatiinic

Website Intelligence MCP

analyze_schema

Validate JSON-LD schema markup on a webpage, count valid and invalid schema objects, and spot structured-data errors that can affect rich results.

Instructions

Validate JSON-LD schema objects of a webpage and count valid and invalid schemas

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations exist, so the description carries the full behavioral burden, and it discloses little: it does not say the tool fetches the page over the network, whether invalid pages error or return counts, how validation rules are chosen, or any rate/permission behavior. Only the validation-plus-count outcome is stated.

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?

A single front-loaded sentence with no filler. It is efficiently sized, though the phrase 'count valid and invalid schemas' slightly blurs validation rules and result reporting in the same clause.

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

Completeness3/5

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

With no output schema and no annotations, the description is the only source of behavioral and result information, and it only hints at the return (valid/invalid counts). For a page-fetching validation tool, an agent still lacks enough to know error modes or result shape.

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 coverage is 0% for the single url parameter, but the parameter is self-evident (a URI) and the phrase 'of a webpage' adds the useful implication that the URL must resolve to an HTML page. This is marginal added meaning over the uri-format schema entry.

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?

States a specific verb (validate) plus resource (JSON-LD schema objects of a webpage) and even the output intent (count valid/invalid). However, it draws no boundary against the sibling analyze_structured_data, which an agent would reasonably confuse with this tool.

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 when-to-use guidance, no prerequisites, and no mention of alternatives such as analyze_structured_data or analyze_content. The agent must infer selection from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.