Generate a multi-angle character sheet from a base image. Returns a jobId; poll with ryla_generation_status.
| Name | Required | Description | Default |
|---|---|---|---|
| nsfw | No | Enable unrestricted fan content generation | |
| angles | No | Optional list of angle presets | |
| characterId | No | Character UUID | |
| baseImageUrl | Yes | Base image URL for PuLID character sheet generation |
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?
No annotations were provided, so the description carries the full behavioral disclosure burden. It correctly and explicitly discloses the asynchronous contract: the tool returns a jobId, not the final image, and requires polling via ryla_generation_status. This is the key non-obvious behavior an agent needs to know before invoking the tool.
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?
The description is two short, purposeful sentences with no wasted words. It front-loads the operation, then gives the return contract and polling step. The content is directly actionable without any repetition of schema details.
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?
For an asynchronous generation tool with no output schema and no annotations, the description covers the essential contract: it generates a character sheet, returns a jobId, and tells the agent to poll with ryla_generation_status. All four parameters are already documented in the schema, so the main missing details like valid angle presets or characterId semantics are optional and do not block 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 schema already documents all four parameters. The description adds only light contextual meaning, like 'from a base image' pointing to baseImageUrl and 'multi-angle' hinting at the angles parameter. This meets the baseline for good schema coverage but does not materially enrich parameter understanding.
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 verb 'Generate' with a specific deliverable ('multi-angle character sheet') and source ('base image'), so the tool's function is unmistakable. It also immediately distinguishes this from siblings like ryla_generate_image, ryla_generate_face_swap, and ryla_generate_base_image by naming the unique output type.
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?
The description makes the usage context clear: use this when you need a multi-angle character sheet from a base image. It also provides the necessary follow-up action, 'poll with ryla_generation_status', which is important for agents to correctly complete the asynchronous flow. It does not explicitly list when-not-to-use alternatives, but the unique deliverable makes the selection fairly unambiguous.
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.