grow_playlist

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

With no annotations provided, the description carries the full burden and handles it well: it explicitly labels the tool as 'Read-only,' describes the candidate-discovery logic, mentions the optional saved-library exclusion, and states that output includes 'evidence.' An agent gets a clear model of side effects (none) and behavior beyond what the schema alone provides.

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?

The description is dense but efficient: three sentences cover purpose, algorithm, exclusions, output, and the read-only workflow. The key scoping phrase ('no recommendations') is front-loaded, and every sentence adds value without filler.

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?

Given the tool's complexity—six parameters, no annotations, no output schema—the description covers purpose, algorithm, and safety. It says the tool 'returns top candidates with evidence,' which is enough for an agent to understand the general result, but it doesn't specify candidate fields or how size and max_results interact. A bit more output-structure detail 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 description coverage is 100%, so the baseline is 3. The description adds some algorithmic context—e.g., 'optionally your saved library' maps to exclude_saved and 'returns top candidates' relates to size—but it does not add meaning beyond what the schema's parameter descriptions already document, which include defaults, ranges, and response_format options.

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?

The description opens with a specific verb and resource: 'Propose tracks to grow one of your playlists.' It clearly differentiates itself from recommendation-based tools by saying 'using ONLY your own listening data (no recommendations)' and lays out the exact algorithm (tracks in >=2 other playlists, artist-overlap boost, exclusions). This leaves no ambiguity about what the tool does or how it is distinct from siblings.

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 gives strong contextual guidance: use this when you want playlist growth from your own listening history, not recommendations, and it prescribes an explicit workflow: 'review the proposals, then call add_to_playlist with the URIs you want.' However, it does not name alternative tools or spell out when-not-to-use beyond the 'no recommendations' constraint, so it's clear but not fully exhaustive.

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