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find_lost_since_snapshot

Compare a current playlist to a saved snapshot to list tracks that have disappeared since that snapshot, helping you spot and recover lost songs.

Instructions

Compare a live playlist against a snapshot and list snapshot tracks that have since disappeared from the playlist Snapshot guide: take_playlist_snapshot (create), list_saved_snapshots (list), read_playlist_snapshot (read), diff_playlist_snapshots / snapshot_new_tracks / snapshot_removed_tracks (diff), restore_playlist_from_snapshot / restore_playlist_plan (restore).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
playlistNoLive playlist to compare (default: the snapshot’s playlist_id)
snapshotYesBaseline snapshot (id, filename, or path)
max_resultsNoMax items to return (default: SPOTIFY_MCP_MAX_ITEMS env or 50)
response_formatNo'concise' = human prose, 'detailed' = more fields in prose, 'json' = raw API objectconcise

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.26.1

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations present, the description carries the behavioral burden. 'Compare' and 'list' strongly convey a read-only operation, and the description clearly states what the output represents: tracks from the snapshot that are now missing from the playlist. It does not detail output structure or side-effect guarantees, but for a lookup-style tool the core behavior is transparent.

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 purpose is front-loaded in a single clear sentence, followed by a compact snapshot-tool guide that earns its place as workflow context. The guide is a dense list of names rather than prose, but it remains concise and useful.

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

Completeness4/5

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

Given the schema fully describing parameters and no output schema, the description provides enough high-level output semantics and workflow context for correct use. It could be more complete by explicitly stating that no playlist or snapshot is modified, but 'compare' and 'list' make this reasonably safe to infer.

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?

The input schema already documents all four parameters with descriptions, including defaults and enums, so the schema carries the parameter burden. The description adds no parameter-specific meaning, so the baseline score of 3 is appropriate.

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 names a precise verb-resource pair: compare a live playlist against a snapshot and list snapshot tracks that have disappeared. This clearly distinguishes it from snapshot-to-snapshot diff tools and 'find_new_since_snapshot' siblings.

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 snapshot guide lists related create/list/read/diff/restore tools, which implies a workflow context, but it never explicitly states when to choose this tool over diff_playlist_snapshots, snapshot_removed_tracks, or find_new_since_snapshot. Guidance remains implicit rather than a direct when-to-use/when-not-to-use rule.

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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