recommend_from_playlist

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

With no annotations, the description carries the full burden and does so well: it discloses the sampling strategy (up to seed_sample_size random seeds), the pooling/ranking approach, and hard output guarantees (never returns songs in Liked Music, the source playlist, or any other playlist). It also flags a caching staleness risk and the refresh_library remedy, which is exactly the kind of behavioral caveat an agent needs before trusting results.

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?

Front-loads the one-line purpose, then layers mechanism, guarantees, and the caching caveat in short paragraphs; every sentence carries information. Slightly verbose in restating the multi-signal pipeline, but no filler sentences.

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?

An output schema exists, so return-value explanation is unnecessary, and the description covers seeding, exclusions, and cache behavior. The main gap is the unexplained `limit` parameter and no mention of failure modes for an invalid playlist_id, but overall it is sufficient to invoke the tool correctly.

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 description coverage is 0%, so the description must compensate for three undocumented parameters. It explains seed_sample_size concretely (random seed count, with whole-playlist fallback) and implies playlist_id's role, but never explains what `limit` (default 20) controls. Partial compensation only.

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?

Names a specific verb and resource ('Recommend new songs based on an entire playlist') and explicitly differentiates its mechanism from the sibling recommend_from_song ('runs the same multi-signal candidate generation as recommend_from_song for each, and pools/ranks'). An agent can distinguish it from recommend_from_song and recommend_from_playlist_for_mood without opening any schema.

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?

Gives clear context for when this tool applies (you have a whole playlist rather than one seed song) and an explicit follow-up call ('call refresh_library() after adding songs to a playlist by other means'), which routes the agent to a sibling when the cache is stale. It stops short of an explicit 'use recommend_from_song when you only have one track' exclusion, so it is strong but not fully enumerated.

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