PhotoForge MCP Server

improve_prompt

Converts a basic user prompt into a high-converting, marketing-grade AI prompt by applying a chosen visual style category such as fashion.

Instructions

Improve a basic user prompt into a high-converting, marketing-grade AI 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": {
      "userPrompt": "<userPrompt>"
    },
    "name": "improve_prompt"
  }
}

Input Schema

Name	Required	Description	Default
`style`	No	Visual style category.	fashion
`userPrompt`	Yes	Simple prompt to enhance.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 24, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It conveys only the transformation intent and says nothing about cost, permissions, determinism, return format, or how the "style" parameter affects the output—significant gaps for a generation tool.

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 single sentence is front-loaded and wastes no words. It is appropriately terse, though its brevity borders on under-specification rather than optimal conciseness.

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 and no annotations, yet the description does not explain what the improved prompt looks like, whether credits are consumed, or how style interacts with userPrompt. For a generation tool with two parameters, this leaves the agent without the context needed to invoke it confidently.

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 both parameters are already documented, establishing a baseline of 3. The description adds no extra meaning about how userPrompt is transformed or how the style category (default "fashion") conditions the result, which would have been valuable context.

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

Purpose4/5

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

The description states a specific verb ("improve") and resource ("user prompt") plus a clear outcome ("high-converting, marketing-grade AI prompt"), so an agent knows exactly what transformation is performed. It does not differentiate itself from siblings such as suggest_styles or generate_caption, so it stops short of a 5.

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?

There is no guidance on when to use this tool versus alternatives like suggest_styles or generate_caption, and no exclusions or prerequisites are given. Usage is only implied by the verb "improve."

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