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llyfn

Spotify MCP Server

add_playlist_items

Add tracks or episodes to any Spotify playlist. Handles large lists by automatically splitting into batches of 100 items, with optional insertion at a specific position.

Instructions

Add tracks or episodes to a playlist. Auto-chunks at 100 items per request.

    Args:
        playlist_id: The Spotify ID of the playlist.
        uris: List of Spotify URIs to add (e.g. ["spotify:track:xxx"]).
        position: Position to insert items (0-based). Appends to end if not specified.
            When chunking, subsequent chunks insert immediately after the previous.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
playlist_idYes
urisYes
positionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals auto-chunking, position handling during chunking, and append behavior. It could mention side effects like duplicates or permission requirements.

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

Conciseness4/5

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

The description is well-structured with a clear action statement followed by an Args section. It is concise but not terse, adding value for each parameter. Minor redundancy with the schema's 'Args' label.

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?

The description covers core functionality and chunking behavior but lacks context on prerequisites (e.g., playlist ownership), error handling, or output. An output schema exists, so return values are not needed.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description adds full meaning for all three parameters: playlist_id (Spotify ID), uris (list with example), position (0-based, append if null, chunking implications). This is excellent semantic enrichment.

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?

The description clearly states the action 'Add tracks or episodes to a playlist', using a specific verb and resource. It distinguishes from sibling tools like remove_playlist_items and reorder_playlist_items by focusing solely on adding.

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

Usage Guidelines4/5

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

The description provides practical usage guidance, such as auto-chunking at 100 items per request and behavior of position during chunking. However, it does not explicitly state when to use this tool over alternatives or exclusion conditions.

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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