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by umsachde

songs_by_artist

Pull real songs by a named artist from YouTube Music, excluding tracks already liked or in any playlist, so you get only new catalog picks.

Instructions

Return actual songs by a specific artist -- a direct catalog pull, not a similarity recommendation like recommend_from_song/recommend_from_playlist.

Resolves artist (a name) to its YouTube Music channel and pulls its real song catalog, excluding anything already in Liked Music OR in ANY of the user's playlists (not just one, unlike recommend_from_playlist's single-seed-playlist exclusion). Read-only: never adds results anywhere.

This is a hard requirement, not best-effort -- if fewer than limit qualifying songs exist after exclusion, this returns however many were actually found rather than padding the list. Check found vs requested in the result to see whether it fell short.

Remix/feature variants of the same underlying song (e.g. a track and its "(feat. ...)" credit under a different videoId) count once, not once per variant -- see variants_collapsed in the result.

The library exclusion set is cached for speed; newly liked songs are always honoured, but call refresh_library() after adding songs to a playlist by other means.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
artistYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses read-only behavior ('never adds results anywhere'), the exclusion set semantics (Liked Music OR any playlist), the hard requirement that it returns fewer than limit rather than padding, and the variant-collapsing behavior. It does not state auth/permission requirements or rate limits, so not a 5.

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?

Multi-paragraph but front-loads the core purpose and sibling differentiation first, then layers in exclusion behavior, shortfall semantics, variant collapsing, and caching. Each paragraph earns its place, though some detail could be trimmed.

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?

The tool has an output schema, so return-value explanation is largely unnecessary, yet the description references result fields ('found' vs 'requested', 'variants_collapsed') to clarify semantics. Covers exclusion behavior, caching caveat, and refresh_library guidance. Could still note artist-resolution failure behavior, but is largely complete.

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. It explains 'artist' is a name resolved to a YouTube Music channel and mentions 'limit' as a qualifying-songs count, adding real meaning. But it does not specify limit bounds, defaults effect, or exact artist-matching semantics, leaving gaps for a 0%-coverage 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+resource ('Return actual songs by a specific artist -- a direct catalog pull') and explicitly distinguishes itself from siblings by contrasting with similarity recommendations like recommend_from_song/recommend_from_playlist. The direct-catalog-pull framing makes it unmistakable.

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

Usage Guidelines5/5

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

Explicitly contrasts this tool with recommend_from_song and recommend_from_playlist, and clarifies the exclusion scope differs from recommend_from_playlist's single-seed-playlist exclusion. It also instructs when to call refresh_library(). Strong when-to-use guidance.

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