RYLA

ryla_generate_video

Generate a Studio video for an existing character (image-to-video). Character must already exist; use ryla_list_characters to find a characterId. Returns a jobId; poll with ryla_generation_status.

Input Schema

TableJSON Schema

Name	Required	Description
`fps`	No	Frames per second (default 24)
`nsfw`	No	Enable unrestricted fan content generation
`seed`	No	Optional seed for reproducibility
`prompt`	No	Optional prompt/description for the video
`useLora`	No	Use character LoRA for face consistency (default true)
`duration`	No	Video duration in seconds
`aspectRatio`	Yes	Video aspect ratio
`characterId`	Yes	Character UUID to generate video for
`realismStyle`	No	Apply photorealism post-processing

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the disclosure burden and it does reveal the critical async behavior: returns a jobId and requires polling via ryla_generation_status. It also clarifies that the input is image-to-video on an existing character; however, it does not mention potential side effects like credit consumption or gallery storage.

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

Conciseness5/5

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

The description is three compact clauses: purpose, prerequisite/lookup, and async result/poll. Every sentence carries necessary routing information and there is no filler.

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

Completeness4/5

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

For a complex 9-parameter tool with no output schema, it gives the necessary invocation workflow: find an existing character, generate, and poll with jobId. It is slightly incomplete in not naming what to do when no character exists, but this is minor given the schema and named sibling 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 the baseline of 3 applies; the schema already describes all 9 parameters including defaults and enums. The description adds only the contextual note that characterId must reference an existing character, but no new parameter-level meaning.

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 names the exact action and resource: 'Generate a Studio video for an existing character (image-to-video)'. This distinguishes it from sibling generation tools such as ryla_generate_image and ryla_generate_character_sheet by emphasizing the existing-character, video-specific scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states the prerequisite ('Character must already exist') and points to ryla_list_characters for finding characterId, plus the follow-up ryla_generation_status polling step. It does not explicitly name an alternative when no character exists, but the routing is clear.

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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TDQS

A3.7/5.0

Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness3/5

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.

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