audio_multisource_optimize

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

No annotations are provided, so the description carries the full burden. It discloses that the tool performs regularization and requires held-out evaluation, but it omits critical behavioral details: what happens if heldOutMatrices are empty (default []), whether the tool is deterministic, computational cost, and what the output format is. The phrase 'require held-out-seat evaluation before acceptance' hints at a validation step but does not explain how rejection occurs.

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 a single, dense sentence that front-loads the primary action. It is concise with no wasted words, but the density makes it somewhat cryptic to nonexperts. Still, it is structured effectively for a technical audience.

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 complex 6-parameter optimization tool with no output schema and no annotations, the description is extremely thin. It fails to explain what the weights are for, how inputs are shaped (complex matrices), what 'acceptance' means, and what the return value is. The tool appears to be a core optimization step, yet the description gives no usage contract.

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?

With 0% schema description coverage, the description must explain parameters. It only hints at regularization (for 'regularization'), training matrices ('training seats', likely trainMatrices), targets (implicit), and held-out matrices ('held-out-seat'). It does not explain maxGainDb, iterations, or the complex number structure. Most parameters remain undocumented, leaving the agent to guess.

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 states a specific verb ('Solve') and a resource ('regularized complex multi-source weights'), and adds scope ('across training seats', 'held-out-seat evaluation'). It is clear enough to distinguish from the sibling 'audio_multisource_physical_optimize', though it does not name alternatives directly.

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 implies a prerequisite: 'require held-out-seat evaluation before acceptance' suggests held-out data must be supplied. However, it does not explicitly state when to use this tool versus other optimization/analysis tools, nor does it mention exclusions or prerequisites for the input matrices.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.