Swap a character face onto a reference base image. Returns the generation result.
| Name | Required | Description | Default |
|---|---|---|---|
| nsfw | No | Enable unrestricted fan content generation | |
| seed | No | Random seed for reproducibility | |
| prompt | Yes | Prompt for the target image | |
| characterId | No | Character UUID | |
| baseImageUrl | Yes | Reference/base image URL to swap the face from |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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, but it only says 'Returns the generation result.' It does not disclose whether generation is asynchronous, whether credits are consumed, or what the result format looks like. The existence of ryla_generation_status suggests async behavior that this description leaves unaddressed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single concise sentence with no filler; both the operation and the return expectation are front-loaded. Every phrase contributes meaningful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a 5-parameter generation tool with no output schema and no annotations, so more context is needed about return format, async behavior, and parameter roles. The description only states a basic purpose and a vague return, which is insufficient for fully correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline applies. The description adds no parameter-level detail beyond repeating 'reference base image,' which is already in the baseImageUrl schema. The phrase 'Swap a character face' could mislead an agent into thinking characterId is required, when the schema marks it optional.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Swap' and identifies the resource ('a character face onto a reference base image'), clearly distinguishing this tool from sibling generation tools like ryla_generate_image and ryla_generate_base_image. The return statement also sets an explicit expectation of an output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to choose this tool over its siblings, nor are prerequisites such as having a characterId or an accessible base image URL mentioned. An agent must infer usage purely from the schema and sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map to a distinct generation type or resource, and the ryla_generate_* family is easy to partition by output type. The main ambiguity is ryla_generate_base_image vs ryla_generate_image, but the description for ryla_generate_image explicitly positions it as the primary Studio tool and clarifies the required characterId.
The ryla_ prefix and snake_case are applied consistently, and generate_* forms a clear pattern for most action tools. A few names deviate from the verb_noun pattern (ryla_generation_status, ryla_server_info, ryla_account_credits_balance), but they are still predictable.
At 11 tools, the set is well-scoped for a media generation server: generation types, status polling, account checks, and character lookup are each covered. The count supports the domain without feeling bloated.
The generation lifecycle is well-covered: jobs can be submitted, polled, status checked, and outputs viewed via the gallery. However, character management is almost entirely absent: there is no way to create, update, or delete a character through the MCP, and the list_characters description explicitly pushes creation to the external app, leaving a notable workflow gap.