List the authenticated user's existing characters (AI influencers) so you can pick a characterId for the other generation tools. There is no tool to CREATE a character -- that happens in the RYLA app at app.ryla.ai.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 provided, the description carries the behavioral disclosure burden. It reveals that the operation is scoped to the authenticated user, that it only lists existing characters, and that character creation is not possible through the API. While it doesn't describe output shape or pagination, the read-only nature is clear from the verb and context.
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?
Two sentences, no filler, and the most important information is front-loaded. The first sentence states action, scope, and purpose; the second adds a critical caveat about creation.
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 a zero-parameter, no-output-schema tool, the description is complete enough for an agent to select and invoke it correctly. It explains why to use it, what it returns conceptually (existing characters/characterIds), and the one common misunderstanding (creation happens elsewhere).
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?
The tool has zero parameters and the schema coverage is 100%, so there is no parameter-level burden for the description to compensate for. The description's mention of returning a characterId for downstream tools adds useful context even though no input parameters exist.
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 a specific verb ('List') with a clear resource ('the authenticated user's existing characters (AI influencers)') and a concrete purpose ('pick a characterId for the other generation tools'). This clearly distinguishes it from the sibling generation, status, and account tools.
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?
It explicitly states when to use the tool: when you need a characterId for other generation tools. It also provides crucial exclusion guidance by noting that no tool creates characters and that creation happens in the RYLA app at app.ryla.ai, preventing the agent from searching for a non-existent sibling.
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.