suggest_pattern_from_audio

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

With no annotations, the description must fully disclose behavior. It states the tool is non-destructive (returns pattern text, not auto-executed) and uses local extraction plus AI, which is good. However, it lacks details on failure modes, required permissions, or what happens if no audio is playing.

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?

Two sentences are concise and front-loaded with key information. However, a slight structure improvement (e.g., listing preconditions) could enhance clarity without adding length.

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 no output schema, the description covers input, process, and output format (pattern text). It lacks explicit prerequisites (audio must be playing) and does not address what happens if audio is not playing or if features extraction fails.

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%, so baseline is 3. The description adds little beyond the schema: it mentions style and role defaults. The role enum values are not elaborated beyond what the schema provides, so no significant added value.

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 clearly states the tool analyzes currently playing audio and suggests a complementary Strudel pattern using AI, with specific features extracted locally. It distinguishes from siblings like generate_bassline or generate_melody by focusing on audio analysis and complement generation.

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?

No explicit guidance on when to use this tool versus other pattern generation tools (e.g., generate_pattern, generate_bassline). The description does not mention prerequisites or exclusions, leaving the agent without context for selection.

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