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

With no annotations, the description carries the full burden and does well: it discloses the async pattern (returns generation.id, poll with wait_for_music) and host-dependent rendering behavior. It omits cost/credit implications and failure/retry behavior, keeping it short of a 5.

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?

Four dense sentences, all front-loaded with the core action and model rules before the return-value note. No filler, though the model enumeration is verbose enough to make scanning slightly heavy.

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 5-param async generation tool with no output schema, the description covers the action, model constraints, prompt semantics, and polling path. Only the two minor params (output_format, idempotencyKey) and cost behavior are unaddressed.

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 only 40%, but the description compensates substantially: it explains what the prompt controls (genre, mood, tempo, vocal style with an example), and the lyrics/model coupling rule. output_format and idempotencyKey remain undocumented in both places.

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?

Opens with a specific verb+resource: 'Generate a music bed or song for a social video, Reel or ad', and names the provider (Mureka AI). An agent can distinguish this from create_voiceover or create_video immediately.

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?

Explains model-dependent usage (song models need lyrics; mureka-7.5 treats lyrics as optional) and points to wait_for_music for polling, which is clear operational context. It does not explicitly state when to prefer this over sibling audio tools, so it falls short of a 5.

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