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playlist_era_profile

Analyze a playlist’s release-era profile: returns a decade histogram, median track age, and a time-capsule verdict so you can understand the playlist’s vintage and era character.

Instructions

Local release-era profile: decade histogram, median track age, and a time-capsule verdict. Pairs with playlist_era slices. Quota: 🟢 1 GET (market refetch disclosed).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNoISO 3166-1 alpha-2 market. When given, items are REFETCHED with this market so album release dates resolve (disclosed second GET).
dry_runNoPreview only: validate inputs and describe exactly what would change without performing it
max_resultsNoMax items to return (default: SPOTIFY_MCP_MAX_ITEMS env or 50)
playlist_idYesPlaylist to profile (ID or spotify:playlist: URI)
response_formatNo'concise' = human prose, 'detailed' = more fields in prose, 'json' = raw API objectconcise
Behavior3/5

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

With no annotations, the description must carry behavioral disclosure. It does add useful cost behavior ('Quota: 🟢 1 GET') and mentions the market refetch, but it doesn't state idempotence, auth expectations, side effects, or the semantics behind 'time-capsule verdict.' The cost disclosure is good, but the safety/behavioral picture is incomplete.

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?

Two dense sentences with no filler: the first identifies the core analytical outputs, and the second adds workflow pairing and quota/cost context. The most important information is front-loaded, and every clause earns its place.

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 tool has no output schema and no annotations, so the description needs to explain return shape and context more fully. It names three outputs but not their form, ordering, or interpretation, and 'local' is ambiguous. For a tool among many playlist-analysis siblings, the description gives a rough idea but leaves notable gaps.

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 100%, so a baseline of 3 is appropriate. The description repeats the market refetch idea from the schema but adds no additional meaning to playlist_id, max_results, dry_run, or response_format beyond what the schema already documents.

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

Purpose4/5

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

The description specifies concrete outputs ('decade histogram, median track age, and a time-capsule verdict') and ties them to a playlist, so it is more specific than the bare tool name. It doesn't explicitly distinguish itself from sibling analytics tools like era_distribution_report or filter_playlist_by_era, which keeps it from a 5.

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?

'Pairs with playlist_era slices' implies a workflow context, and the quota line gives operational guidance, but there is no explicit when-to-use or when-not-to-use statement. The agent is not told to prefer this over closely related sibling tools such as playlist_staleness_score or era_distribution_report.

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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