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

With no annotations, the description carries the full burden of behavioral disclosure. It explains chance semantics (100 always plays, 50 half the time, 0 never), the five distribution modes, seed reproducibility, and the return value (per-track note counts and chance range). It falls short of a 5 because it does not clarify the meaning of unit_index/track_index/region_index or whether the operation destructively modifies the original notes.

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 well-structured with a clear title, use-case list, mode explanations, parameter definitions, and concrete examples. Despite being long, every section earns its place, front-loading the primary purpose and using bullet points and examples to convey complex information efficiently.

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?

Given the tool's complexity and the absence of annotations, the description covers the core purpose, all modes, parameter meanings (for the main parameters), use cases, and return value. It is slightly incomplete because it omits explicit explanations of the index parameters, and the output schema is not shown, leaving return-value details to a single sentence.

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 0%, so the description must compensate. It thoroughly explains mode (including all five distributions), min_chance, max_chance, and seed, but it does not explain unit_index, track_index, or region_index beyond showing them in examples. This leaves three of seven parameters without explicit semantic guidance.

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 opens with 'Randomize note playback probability (chance) — generative variation,' clearly identifying the tool's function and distinguishing it from siblings like randomize_note_durations. It provides a specific verb+resource (randomize note playback probability) and explains the outcome (sets a random chance value for each note).

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 'Perfect for' section explicitly lists four generative use cases (ghost notes, generative melodies, call-and-response, evolving textures), giving clear context for when to use the tool. However, it does not mention when not to use it or name alternative tools for different scenarios.

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