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Nexscope Ecommerce Data MCP

Product TRO Detection

nexscope_maidalv_product_tro_detection

Analyzes a main product image with optional evidence images, text, keywords, and language.

Input Schema

NameRequiredDescriptionDefault
ipImagesNoUp to 3 intellectual-property evidence images, each a public URL or image data URI.
languageNoLegal report language: zh (default) or en; affects report only.
ipKeywordsNoUp to 20 intellectual-property keywords.
descriptionNoProduct description, preferably the title; maximum 1,000 characters.
referenceTextNoSimilar-product text, maximum 1,000 characters.
referenceImagesNoUp to 3 reference images, each a public URL or image data URI.
mainProductImageYesMain product image as a public URL or data:image/...;base64, URI; maximum 1,000 characters.
otherProductImagesNoUp to 5 additional product images, each a public URL or image data URI.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.6/5.0
Behavior2/5

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

Annotations declare readOnlyHint=false and openWorldHint=true, so the tool creates something (likely a job or credit-consuming analysis), yet the description says nothing about that, about async/polling behavior (cf. nexscope_media_task_result), or about cost. It restates input categories rather than disclosing behavior beyond the 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?

A single short sentence with no filler, and the required input (main image) is front-loaded ahead of the optional evidence inputs. It is efficient but bordering on under-specification.

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?

There is no output schema, so the description must explain what a TRO detection returns, but it does not. For an 8-parameter, non-read-only detection job whose result shape is entirely undocumented, the definition leaves an agent unable to interpret results or predict side effects.

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% and every one of the 8 parameters carries its own description with limits and formats, so the baseline is 3. The description only re-lists the optional input categories and adds no semantics the schema lacks.

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

Purpose3/5

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

The description supplies a verb ("Analyzes") and a resource ("a main product image"), but never explains what "TRO detection" actually produces — IP/trademark infringement risk, logo match, or general image QA. With many sibling IP-detection tools (nexscope_ruiguan_trademark_graphic_detection, nexscope_text_trademark_detector), the agent cannot tell from the text which one covers this case.

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?

No when-to-use or when-not-to-use guidance, and no alternatives named. The description lists optional inputs but never says why an agent would add keywords versus reference images, or when to prefer a sibling detection tool.

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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