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envious-canvas

envious-canvas

by saurabhav88
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

canvas_inspect_asset

Read-onlyIdempotent

Inspect an image with AI vision: describe contents, extract structured attributes, QA check fidelity.

Instructions

Analyze an image using AI vision — describe contents, extract attributes, QA check.

Use when: The user wants to understand an image, verify product fidelity, extract structured attributes, or QA check a generated asset.

Do not use when: The user wants to generate or modify images.

Example: canvas_inspect_asset(image="/path/to/bag.png", question="Does this match the original product shape?")

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": {
      "params": "<params>"
    },
    "name": "canvas_inspect_asset"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds context about the types of analysis. No contradictions; description complements annotations.

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?

Concise: three sentences plus use-case guidelines and an example. No wasted words; front-loaded with purpose.

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

Completeness5/5

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

Given annotations (read-only, nondestructive) and an output schema, the description covers purpose, usage guidance, and parameter hints adequately for a focused analysis tool.

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

Parameters4/5

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

Description provides semantic meaning for parameters via example and mentions structured attribute extraction. Schema descriptions are detailed, but the tool description adds usage context beyond the schema.

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?

Description clearly states 'Analyze an image using AI vision' with specific actions (describe, extract, QA check). Distinguishes from sibling tools like canvas_create_image and canvas_edit_image.

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

Usage Guidelines5/5

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

Explicitly provides 'Use when' and 'Do not use when' sections, guiding the agent on appropriate contexts and alternatives. Includes a concrete example.

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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