auto_analyze_audio

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

The description states the tool is synchronous and returns analysis directly without a job_id, and lists the specific return values (peak level, noise floor, duration, clipping status, recommendation). This provides transparency about the immediate nature and output format. It does not explicitly state it is read-only, but the term 'analyze' implies no modification, and no side effects are mentioned.

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 concise and well-structured: first sentence states purpose, second clarifies synchronous nature, third lists return values, and final line gives prerequisite. No redundant or vague phrasing.

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 lack of parameters and output schema, the description provides sufficient context by explaining the return values and prerequisite. It clearly tells the agent what to expect and what to do before calling. The mention of 'auto_ pipeline' is understood from the sibling tool list, and the tool's role as a recommender is clear.

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?

The input schema has zero parameters, so there is nothing to explain. The description adds no ambiguity about parameters; the empty schema is fully covered.

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 the current audio track and recommends the best auto_ pipeline to use. It specifies the action (analyze) and the resource (current audio track), and names the output (recommendation). This distinguishes it from sibling tools that directly apply effects or perform other operations.

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 includes an IMPORTANT prerequisite: load audio into Audacity before calling. It also implies usage as a decision-support tool before invoking an auto_ pipeline. However, it does not explicitly contrast with alternatives such as calling a pipeline directly or using other analysis tools, leaving some inference to the agent.

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