Rank compatible UVR models from a task description and return a task-based selection for vocal removal, stem separation, or audio restoration.
Rank compatible supported models and expose the task-based selection.
| Name | Required | Description | Default |
|---|---|---|---|
| intent | Yes | Task in your own words, English or German. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this is a safe, idempotent, read-only, non-destructive operation, so safety is covered. The description adds that models are filtered for compatibility and ranked, and that selection is task-based — useful behavioral context, though 'compatible' and the ranking method are left unexplained.
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?
It is a single sentence with no wasted preamble, which is good, but the trailing clause 'expose the task-based selection' is vague enough that it doesn't fully earn its place. Front-loading 'Rank compatible supported models' is effective.
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 one-parameter read-only tool with an output schema and full annotation coverage, the description is minimally adequate. However, it leaves important context unstated: what compatibility means, how ranking works, and why an agent would choose this over list_models.
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 coverage is 100% and the single 'intent' parameter is fully described in the schema. The description's phrase 'task-based selection' loosely ties the intent parameter to the ranking behavior but adds no format or semantic detail beyond what the schema already says.
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 verb 'Rank' and resource 'compatible supported models' give a rough sense of purpose, but the second clause ('expose the task-based selection') is jargon that doesn't clarify what the tool actually returns. It never names or distinguishes itself from the sibling list_models, so an agent cannot confidently tell which to pick.
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?
There is no explicit when-to-use, when-not-to-use, or alternative named. The only hint is the word 'task-based,' which implies usage when you have a task intent, but that is inference rather than guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.