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mcpGetImage

mcpGetImage

by raulisai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

refine_candy_prompt

Transform a candy image request into a structured white-background product prompt for generating product images.

Instructions

Refine a candy image request into a white-background product prompt.

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": {
      "request": "<request>"
    },
    "name": "refine_candy_prompt"
  }
}

Input Schema

NameRequiredDescriptionDefault
styleNofotografia de producto premium
requestYes
compositionNoproducto centrado, vista tres cuartos
extra_detailsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states the core function and does not mention any side effects, return values, or limitations. For a transformation tool, there's no context about output format or relationship to other tools, so transparency is minimal.

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 is a single sentence with no wasted words. It is front-loaded with the verb 'Refine' and provides the essential purpose efficiently.

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?

With 4 parameters and no annotations, the one-sentence description is insufficient to fully understand tool usage. It does not explain parameter semantics, default values, or how the refined prompt should be used alongside sibling tools. Though an output schema exists, the description itself lacks enough context.

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

Parameters1/5

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

Schema description coverage is 0% (none of the 4 parameters are described). The description provides no parameter-level semantics, failing to explain what 'request', 'style', 'composition', or 'extra_details' mean or how they interact. Since coverage is low, the description should compensate but doesn't.

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 uses the specific verb 'refine' and states the resource ('candy image request') and outcome ('white-background product prompt'). This clearly distinguishes it from sibling tools 'generate_candy_image' and 'remove_background'.

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 usage (when you need to turn a candy request into a product prompt) but does not explicitly state when-not-to-use or mention alternative tools. No exclusions are given, so guidance is present but implied rather than explicit.

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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