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ToolTrace-io

ToolTrace MCP Server

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tooltrace_schema

Extract JSON-LD structured data from a webpage and return schema.org entities like Article, Product, Organization, FAQ, and BreadcrumbList.

Instructions

Extract JSON-LD structured data from a webpage. Returns schema.org entities like Article, Product, Organization, FAQ, BreadcrumbList, etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic webpage URL
modeNo'normalized' deduplicates entities. 'raw' preserves original JSON-LD blocks.normalized
renderNoRendering mode. 'never' = fast static fetch (1 credit). 'auto' = static first, browser if needed. 'always' = browser rendering (5 credits).auto
wait_untilNoBrowser navigation milestone. Only used with browser rendering.
wait_for_selectorNoCSS selector to wait for on rendered pages.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the output type (schema.org entities) and that it extracts from a webpage, but says nothing about limitations such as JSON-LD-only support, dynamic-rendering behavior, credit implications, or failure cases. This is acceptable for a simple read-style tool but leaves notable gaps.

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 compact two-sentence definition with no filler and the core action front-loaded in the first clause. It loses a point only because the 'etc.' tail adds little and could be replaced with a more precise statement about normalized/raw modes.

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?

For a tool with five parameters, three enums, and no output schema, the description gives only a high-level outcome. It does not explain how mode affects returned data, when rendering matters, or what the raw output looks like; some of this lives in the schema but the overall context is thin.

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 description coverage is 100%, so the schema already documents all five parameters (url, mode, render, wait_until, wait_for_selector). The description adds no parameter-level meaning beyond mentioning entity categories, which is not needed because the schema does the heavy lifting.

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 names a specific action ('Extract'), a specific resource ('JSON-LD structured data from a webpage'), and enumerates concrete schema.org entity types. This clearly distinguishes it from sibling tools focused on sitemaps, metadata, links, tech stack, or generic extraction.

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 intended use is implicitly clear: select this tool when JSON-LD/schema.org entities are needed from a URL. However, it never explicitly states when not to use it or names an alternative such as tooltrace_extract for non-JSON-LD extraction, leaving the routing decision largely to inference.

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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