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geo-inspector-mcp

geo-inspector-mcp

by Bigsupe55
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Detect JSON-LD schema markup

detect_schema_markup

Extract JSON-LD structured data from a webpage and identify key schema.org types for AI discoverability and entity disambiguation.

Instructions

Extract JSON-LD structured data from a page and inventory the schema.org types found. Flags the types that matter for AI discoverability (Organization, WebSite, Article, FAQPage, BreadcrumbList, Person) and sameAs entity disambiguation.

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": {
      "url": "<url>"
    },
    "name": "detect_schema_markup"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesWebsite URL. Scheme is optional, example.com works.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4/5.0
Behavior4/5

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

Without any annotations, the description effectively communicates the tool's behavior: extracting JSON-LD, inventorying types, flagging important ones, and performing sameAs disambiguation. It is transparent about its functionality, though it doesn't mention edge cases or error handling.

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?

The description consists of two concise sentences. The first sentence covers the core function, and the second adds important specifics about flagged types and disambiguation. No extraneous information.

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

Completeness4/5

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

Given no output schema, the description explains that the tool extracts JSON-LD and provides an inventory of types with flags. This covers the main output expectation. However, it could be more explicit about the return format or what happens when no JSON-LD is found. Still, it's sufficiently complete for a single-parameter tool without annotations.

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?

The schema provides 100% coverage for the single parameter 'url' with a clear description. The tool description adds context that the URL points to a page containing JSON-LD, but this is inherent from the tool's purpose. No additional parameter semantics beyond the schema are needed.

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 extracts JSON-LD structural data and inventories schema.org types, specifically naming the relevant types for AI discoverability. This distinguishes it from sibling tools like check_robots_txt or fetch_llms_txt which handle other SEO aspects.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used to inspect schema markup on a page but does not explicitly state when to use it versus alternatives. No exclusion criteria or specific context is provided to guide tool selection among 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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