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generate_photo

Generate realistic AI photos from text prompts with customizable style, pose, lighting, and background. Use face references for personalized results.

Instructions

Generate a high-quality realistic AI photo. Consumes 3 credits.

Input Schema

NameRequiredDescriptionDefault
poseNoPose styles (e.g. front, side-profile, smiling, etc.)
styleNoVisual style presets (e.g. headshot, linkedin, casual, etc.)
promptYesThe detailed text prompt describing the photo to generate.
qualityNoQuality preset.standard
lightingNoLighting styles (e.g. studio, soft, golden-hour, etc.)
providerNoAI Model provider (seedream-5-pro, fal-ai-grok, flux-dev).seedream-5-pro
userImageNoOptional user face reference image (Base64 data URL or remote URL).
backgroundNoBackground description (e.g. professional office, outdoor park, etc.)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

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 full behavioral burden. It usefully discloses the 3-credit cost, but says nothing about whether the result is saved, how long generation takes, or what happens on failure.

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?

Two short sentences with no filler, and the core purpose is front-loaded. For an eight-parameter tool with no annotations, however, the treatment is arguably too terse rather than optimally sized.

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 eight parameters, no annotations, and no output schema, the description is insufficiently complete. It covers purpose and credit cost but leaves out return behavior, persistence, and choice among related tools.

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 coverage is 100%, so all eight parameters are already described in the input schema. The description adds no parameter-specific meaning beyond the schema, which is the baseline expectation when coverage is complete.

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?

Uses a specific verb ("Generate") and resource ("AI photo") with an added quality adjective. It does not differentiate from nearby siblings like generate_video, upscale_photo, or change_background, but the core purpose is unmistakable.

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?

The description gives no conditions for when to use this tool versus alternatives such as upscale_photo, change_background, or generate_video. An agent must infer usage from the name alone.

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