like_track

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Add') but doesn't mention whether this requires authentication, if there are rate limits, what happens if the track is already liked, or what the response looks like (e.g., success/failure). For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.

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 a single, efficient sentence that gets straight to the point with no wasted words. It's appropriately sized for a simple tool and front-loads the essential information about what the tool does.

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 moderate complexity (a mutation with one optional parameter), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and parameter behavior but lacks details on authentication requirements, error handling, or response format, which would be needed for full completeness in this context.

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 description adds some meaning by explaining that the track_id parameter is optional and defaults to the current track if not provided. However, with 100% schema description coverage (the schema already documents the parameter as optional with the same explanation), this doesn't provide additional value beyond what's in the structured data. The baseline of 3 is appropriate when the schema does the heavy lifting.

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 action ('Add') and resource ('current or specified track to your Liked Songs'), distinguishing it from sibling tools like 'add_to_playlist' (which adds to a specific playlist) and 'unlike_track' (which removes from Liked Songs). It specifies the exact destination ('Liked Songs'), making the purpose unambiguous.

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 provides clear context for when to use this tool: to add tracks to the Liked Songs collection. It distinguishes from 'add_to_playlist' by specifying the destination, but doesn't explicitly state when NOT to use it or mention alternatives like 'unlike_track' for removal, which would be helpful for complete guidance.

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