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never_played_saved

Find saved Spotify tracks absent from your recently-played window. Because recently-played covers only about your last 50 plays, this surfaces songs not played lately rather than never played.

Instructions

List saved tracks absent from your recently-played window. Honest bounds: recently-played only covers roughly your last 50 plays, so this is "not played lately", not "never played". Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoPreview only: report the request cost of the scan without performing it (default false)
scan_capNoMax saved items to walk per paginated source (default: SPOTIFY_MCP_FETCH_ALL_CAP)
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
Behavior4/5

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

With no annotations provided, the description carries the full burden and it does well: it explicitly states 'Read-only' and spells out the key behavioral limitation that the recently-played window covers only roughly the last 50 plays. It also corrects the potentially misleading tool name. It does not disclose scan cost or pagination, but the schema's dry_run parameter already signals scan-cost semantics.

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?

Three short sentences, each earning its place: the operation, the honest bounds, and the read-only flag. The most decision-relevant information is front-loaded in the first sentence. No filler or repetition of schema content.

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 core purpose, the key caveat, and the read-only safety trait are covered, which is solid for a no-annotation, no-output-schema tool. However, it omits any description of the return shape (no output schema exists) and does nothing to route the agent among the many similar library-analysis siblings. Adequate but with clear 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 the schema fully documents all four parameters (dry_run, scan_cap, max_results, response_format), warranting the baseline 3. The description adds no parameter-specific semantics beyond the general notion of scanning saved tracks against a recently-played window. It neither compensates nor detracts from the schema.

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 and resource: 'List saved tracks absent from your recently-played window.' The honest-bounds clarification ('not played lately', not 'never played') removes ambiguity from the tool name and sets accurate expectations. This distinguishes it from siblings like get_recently_played and get_saved_racks without needing to open their schemas.

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

Usage Guidelines2/5

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

No alternatives are named and no when-to-use vs when-not-to-use guidance is given, despite many plausible siblings (get_recently_played, last_heard, dead_library_finder). The 'Honest bounds' caveat is about data-source semantics, not a prescription for choosing this tool. An agent must infer applicability from the name and description alone.

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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