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artistwatch_new_additions

Monitor followed artists for new album or single releases within a configurable window. Receive alerts for releases from artists you follow.

Instructions

Watches your FOLLOWED artists for new material: walks /me/following, probes each artist's latest release and flags those released within the last N days. Quota: 1 cursor walk + 1 small albums call per followed artist.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoFreshness window in days. Default: 30
artists_capNoMax followed artists probed. Default: 30
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, the description carries the behavioral burden and delivers real disclosure: it states it walks /me/following, probes each artist's latest release, and quantifies the quota as 1 cursor walk + 1 small albums call per artist — useful cost and latency context an agent wouldn't get from the schema. It falls short of full transparency by not warning about sequential-call latency when artists_cap is high and not clarifying what the flagged output looks like.

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 tight sentences front-load the purpose ('Watches your FOLLOWED artists for new material') before the mechanics and quota disclosure. Every clause earns its place, including the valuable quota note, and there is zero filler.

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?

For a read tool with no annotations and no output schema, it covers the core behavior and quota cost well. But it never describes the return shape ('flags' is vague), gives no routing among the many watch/check/digest siblings, and omits a latency caveat for high artists_cap values — gaps that matter because neither annotations nor an output schema exist to fill them.

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 coverage is 100%, so the baseline applies; the description's 'last N days' and per-artist probe narrative do align conceptually with the days and artists_cap parameters. It adds no syntax, format, or interaction detail beyond the schema, which is acceptable at full coverage but not additive.

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 names a specific action (watches, probes, flags) and a specific scope (FOLLOWED artists via /me/following), tied to a freshness window, so an agent can tell what it does. However, with near-overlapping siblings like check_artist_releases, artist_release_digest, and new_music_from_saved_artists in the toolset, it never states how it differs from them, stopping short of full disambiguation.

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 mechanism and quota note imply a monitoring use case, so an agent can infer roughly when it applies. But there is no explicit when-to-use guidance, no exclusions, and no mention of alternatives despite several close siblings in the same domain, leaving routing largely to inference.

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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