shelf

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

No annotations exist, so the description carries the full burden — and it delivers meaningful behavioral context. It discloses the two-layer data structure (built-in vs. user-added), the tag-filtering semantics, the empty-tag-returns-everything behavior, and the return tuple shape including source and picked_by fields. It explains what data a user sees and how curation is tracked. Loses a point for not addressing staleness, update semantics, or potential side effects (though clearly a read).

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?

Well-structured with clear Args/Returns sections and a useful three-line intro. Every sentence earns its place — the two-layer explanation, the rss_read integration hint, and the param/return docs. The 'engawa' poetic opener is charming but slightly obscure; overall, tight and efficient.

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 single-param, no-annotation, no-output-schema tool, the description covers the param semantics, return shape, and data model well. Minor gaps: field-level semantics of entries (e.g., what 'picked_by' means, how 'title' relates to 'name'), and sorting/pagination behavior. But given the tool's simplicity and the sibling ecosystem, it's quite 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?

With 0% schema coverage, the description fully compensates for the sole 'tag' parameter. It provides the exact enum-like values (science/ocean/longform/architecture/wonder), documents that empty string means 'return everything', and clarifies filtering semantics. This is exactly what parameter documentation should look like when the schema is sparse.

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 clearly identifies the tool as a curated collection of feed sources (name/one-liner/tags) that feeds into rss_read. It distinguishes itself from siblings by being the base listing operation among shelf_add/shelf_remove/shelf_suggest, though it doesn't explicitly state 'list all feeds'. The engawa metaphor adds ambiguity, but the practical purpose is clear.

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 explains the data model (two layers: built-in picks plus user additions via shelf_add) and points to feeding names into rss_read, implying usage context. However, it never explicitly states when to use this vs. alternatives like 'use rss_read to read content' or 'use shelf_suggest for recommendations'. Usage is implied through ecosystem references, not stated.

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