Track Fit

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

Without annotations, the description carries the full behavioral burden and does well: it explains the render/score loop, the per-iteration rendering cost, and that apply=True writes the best values into the track. It still omits what happens when apply=False returns and any precondition on stem_map/reference setup, but the core mutation and computational profile are clear.

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 dense but front-loads the purpose and then adds operational detail, with no obvious filler. The embedded param examples make it slightly harder to scan than a bulleted list, but it remains appropriately sized for a complex tuning tool.

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?

With no annotations, an output schema, and 7 parameters at 0% schema coverage, the description is substantially complete: it covers the tuning workflow, parameter syntax, budget, and mutation behavior. It stops short of naming alternatives or explaining seed, but the remaining gaps are modest.

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 0%, so the description must compensate. It explains the params object syntax ('inst.<param path>', 'fx.<i>.<param>', 'volume_db', 'pan'), the bars range, the iters budget, and apply behavior. Seed and project/track are not addressed, but the complex parameters are well documented.

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?

States a specific verb+resource: 'Tune a track IN CONTEXT' by rendering its stem group and scoring against a reference stem. This scope clearly separates it from sibling tools like instrument_fit and eq_match, which do not describe whole-stem, in-context rendering against a reference.

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 implies the tool is for tuning track parameters against a reference, and gives a workable bars/iterations budget. However, it never names alternatives or states when to prefer track_fit over instrument_fit, eq_match, or levels_from_ref, so an agent must infer the routing.

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