PhotoForge MCP Server

suggest_styles

Recommend visual styles and rendering qualities for a chosen prompt category, helping users pick suitable aesthetics before generating images.

Instructions

Suggest popular visual styles and rendering qualities for a prompt type.

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": {
      "category": "<category>"
    },
    "name": "suggest_styles"
  }
}

Input Schema

Name	Required	Description	Default
`category`	Yes	Category.

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?

No annotations are provided, so the description carries the full behavioral burden, yet it says nothing about whether the call is read-only (only implied by 'suggest'), whether it consumes credits, or what the response contains. For a suggestion tool, knowing the shape of the returned style list is essential and is absent.

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 front-loaded sentence with no filler. It is efficient, though its brevity borders on under-specification rather than tight structure.

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

Completeness3/5

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

The tool is simple (one enum param, no nested objects), so not much is required, but with no output schema and no annotations the description should at least sketch what a 'suggestion' looks like and whether it feeds into generate_photo or improve_prompt.

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 the baseline is 3, but the schema's own description is only the tautology 'Category.' The description adds the phrase 'for a prompt type', which hints at meaning without explaining how the professional/dating/creator/travel/fashion enum values affect the suggestions.

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?

States a specific verb ('suggest') and resource ('popular visual styles and rendering qualities') scoped to a prompt type. An agent can grasp the intent immediately, though it does not differentiate itself from neighbors like improve_prompt or recommend_photo_pack, and 'prompt type' only loosely matches the schema's 'category' enum.

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 guidance on when to call this versus improve_prompt, recommend_photo_pack, or generate_photo, and no prerequisites or sequencing hints. The usage is only inferable from the verb itself.

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