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

The description adds behavioral context beyond annotations: personalization based on search history, spread parameter semantics, and the concept of a 'starting track.' However, it fails to disclose how the starting track is determined (e.g., from current playback, user context, or an implicit source), which is critical given that the input schema lacks a track parameter. This gap reduces transparency despite the helpful spread details.

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 compact (three sentences), front-loaded with the core purpose ('Discovery: pick a starting track...'), and efficiently covers personalization and spread options without unnecessary words. Every sentence adds value, making it easy for an agent to parse quickly.

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 has only one optional parameter and an output schema (covering return values), the description adequately explains the spread parameter and personalization. However, it omits how the starting track is provided (critical for invocation) and what happens for non-signed-in users. These gaps prevent it from being fully self-contained, even with annotations present.

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?

With 0% schema description coverage, the description must fully document the only parameter ('spread'). It does so excellently, explaining each enum value with vivid, intuitive analogies ('familiar', 'fresh', 'adventurous', 'random') and noting the default. This goes well beyond the enum names, providing clear, actionable semantics for the agent.

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's purpose: 'pick a starting track and show similar tracks.' This is a specific verb-resource pairing. However, it does not explicitly differentiate from sibling tools like 'find_similar' or 'surprise_me', which could overlap in functionality. The personalization and spread details add context, but sibling differentiation is absent.

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 usage context by detailing personalization for signed-in users and the meaning of each spread value. It offers implicit guidance on when each spread setting is appropriate (e.g., 'familiar' vs 'random'). However, there is no explicit comparison to siblings or advice on when to use this tool versus alternatives, leaving the agent to infer usage from the behavior description alone.

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