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mio_ai_face_swap

Face Swap — Choose a source face and a target photo, run the face swap, and download the result after comparing it with the target.. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior4/5

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

No annotations exist, so the description carries the behavior burden, and it discloses the Modal worker dispatch, variable credit cost, exclusion from Day Pass/welcome credits, file deletion, retention/audit URL, and credit-pack model. It still omits latency, async behavior, and input image requirements, so it is not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The first sentence front-loads the purpose and workflow, but the remainder includes account-level marketing ("All three credit-based workspaces...") and a stray double period. Some billing/retention details earn their place; the credit-pack explanation does not.

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?

For a zero-param tool with no output schema, the description covers purpose, credits, and data retention. It does not explain how the source/target images are supplied despite the empty schema, or exactly how the result is returned, leaving invocation mechanics implicit.

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 tool has zero parameters, so the baseline is 4. The description mentions "source face" and "target photo," which are conceptual inputs not represented in the empty schema, but adds no parameter syntax or naming.

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 opens with "Face Swap" and a concrete workflow (source face, target photo, run, download), so the verb and resource are unambiguous. It stops short of distinguishing this from the sibling face_enhancer or other photo tools, so it is clear but not fully differentiated.

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 gives a three-step usage flow ("choose... run... download...") but never states when to choose face swap over face_enhancer or other alternatives, nor any exclusions. Usage is implied by the workflow rather than explicitly routed.

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

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