playlist_fill_from_search

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses the algorithm ('round-robin one pick per query per pass, first unseen track match wins, chunked adds') and the quota ('len(queries) searches + chunked adds'). The mutation side effect (adding tracks to a playlist) is clearly implied by 'Grow' and 'adds'. It could mention reversibility or failure behavior, but the core side effects and cost model are covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences pack purpose, algorithm, sibling relation, and quota with zero filler. The front-loaded purpose sentence immediately tells the agent what this tool does, and every subsequent clause earns its place.

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

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

For a 6-parameter tool with no output schema, the description explains the selection algorithm, quota, and relation to a sibling. It doesn't break down exact return behavior, but response_format is already covered by the schema. The complement to grow_playlist gives necessary routing context, though a bit more on when to prefer the listening-data variant would make it fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% so the schema already documents all six parameters. The description adds a few semantic hooks — 'N items' ties to target_count, 'queries you supply' ties to queries, and the quota connects query count to cost — but it does not meaningfully elaborate on market, dry_run, or response_format beyond what the schema already states.

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

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

States a specific verb and resource ('Grow a playlist to N items from search queries'), and immediately distinguishes itself from the sibling tool grow_playlist ('Complements listening-data grow_playlist'). The selection strategy (round-robin, first unseen match) makes its behavior unmistakable.

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

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

The description names an alternative ('grow_playlist') and implies the condition that selects it: this tool uses explicit search queries, while grow_playlist uses listening data. It doesn't spell out explicit 'use this when...' exclusions, but the contrast is clear enough for an agent to route correctly.

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