universal-image-mcp

prompt_guide

Get best practices and examples for writing effective image generation prompts. Craft detailed prompts to produce better results.

Instructions

Get best practices and examples for writing effective image generation prompts.

Returns guidelines for crafting detailed prompts that produce better results.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "prompt_guide"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.3Jul 31, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden of disclosure. It states that the tool 'returns guidelines', which is a clear behavioral outcome. However, it does not explicitly mention side effects (likely none) or whether any authentication is needed, leaving some ambiguity for a tool with zero annotation support.

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 extremely concise—two sentences with no redundancy. The first sentence front-loads the purpose, and the second clarifies the return value. Every word contributes meaning.

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 the tool's simplicity (no parameters, no nested objects) and the presence of an output schema, the description covers the essential context. It clearly differentiates from sibling tools and fully explains what the tool delivers. The sibling names provide additional context that aligns with the description.

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?

The input schema has no parameters, and the schema description coverage is 100% (trivially). Per the baseline for 0 parameters, a score of 4 is appropriate since no parameter semantics are needed and the description doesn't need to compensate.

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's function with a specific verb ('Get') and resource ('best practices and examples for writing effective image generation prompts'). It distinguishes itself from siblings like generate_image and transform_image by focusing on guidance rather than action.

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 for improving image prompt quality but offers no explicit when-to-use or comparison with alternatives. With sibling tools present, more explicit guidance would be helpful, but the context makes the primary use case evident.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server