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append_to_playlist

Add LLM-generated song and artist picks to any existing Spotify playlist by providing its ID, enabling programmatic playlist updates.

Instructions

Append LLM-proposed {title, artist} picks to an existing playlist by id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tracksYes
playlist_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It discloses the core mutation (append modifies an existing playlist in place), but says nothing about prerequisite track resolution, duplicate handling, failure behavior for unresolvable picks, or required authorization scope. For a write operation with zero annotation coverage, this is a meaningful gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single 13-word sentence that leads with the verb and packs in the target resource, content shape, and identifier. No wasted sentences or redundant restating of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter tool with an output schema present, the description covers the essentials: what is appended and where. But it leaves unaddressed the pipeline relationship suggested by sibling resolve_tracks — whether picks must be resolved or validated before appending — and what happens when a pick cannot be found. Adequate core, but with gaps an agent would reasonably encounter.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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. 'existing playlist by id' gives meaning to playlist_id, and '{title, artist} picks' gives meaning to the tracks items. However, the schema allows additionalProperties of any string key, and the description does not clarify whether only title/artist are allowed or whether extra keys are ignored, leaving partial ambiguity about the tracks structure.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Append), resource (existing playlist), and scope (by id), with content shape qualified as {title, artist} picks. This implicitly distinguishes it from siblings: it modifies an existing playlist rather than creating one (create_playlist_from_tracks), playing one (play_playlist), or reading one (get_playlist_tracks). The 'LLM-proposed' qualifier is slightly odd but does not obscure the operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The usage context is implied: you need an existing playlist ID and picks to append, so it naturally follows creating or finding a playlist. However, no explicit when/when-not guidance is provided — it does not name alternatives such as create_playlist_from_tracks for new playlists or resolve_tracks for preparing picks, leaving routing to inference.

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

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