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aaddyy_face_swapper

Swap faces between photos by uploading a source face and target body image to generate a photorealistic AI result.

Instructions

Swap faces between photos with AI. Upload a source face and a target body image to get a seamless, photorealistic face swap result. (~11 credits)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptNo
aspect_ratioNo
output_formatNo
source_image_urlYes
target_image_urlYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does add real context: the ~11 credit cost, the required input pairing, and a claim about output quality (seamless, photorealistic). It still omits retention/permission behavior, failure modes, and processing expectations for an image upload 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?

Three short sentences, front-loaded with the action and then the input/output expectation and cost. No filler, though the cost parenthetical sits slightly awkwardly at the end rather than with the input requirements.

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?

For a 5-parameter tool with no annotations, no output schema, and zero schema documentation, the description covers only the core happy path. The three optional parameters and any output/return behavior remain completely unspecified.

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

Parameters2/5

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

Schema description coverage is 0% and all five parameter descriptions are empty, so the description must compensate. It only maps the two required URLs to concepts (source face vs. target body), leaving prompt, aspect_ratio, and output_format entirely undefined for the agent to guess.

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 states a specific verb and resource (swap faces between photos) and contrasts with the surrounding image-editing siblings by naming the exact operation. An agent can distinguish this from background_remover, upscale_image, or image_generator without opening the schema.

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?

It implies when to use the tool by naming the two required inputs (source face, target body image) and quoting a credit cost, which signals a paid generative operation. However, it never states exclusions or point to alternative siblings, so the routing guidance is only implied.

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