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shelf_add

Add a website or RSS feed to your shelf by providing a URL. Optionally set a name, note, tags, and picked_by attribution; the tool discovers and verifies the feed before saving.

Instructions

Add a source to the shelf. Give any site address — if it is already a feed it is used as-is; otherwise its RSS/Atom feed is discovered (page-declared first, common paths as fallback) and verified with a real fetch before anything is saved.

Args: url: full http(s) address of a site or feed (e.g. https://nautil.us) name: shelf name (optional; lowercase letters/digits/hyphens, 2-40 chars; empty = derived from the domain, collisions get -2) note: one-line description (optional) tags: comma-separated, from science/ocean/longform/architecture/wonder picked_by: free-form provenance — who asked for this source (optional)

Returns: {ok, name, feed_title, feed_url, note}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
nameNo
noteNo
tagsNo
picked_byNo
Behavior4/5

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

With no annotations provided, the description carries the full burden and does an excellent job: it discloses feed discovery logic (page-declared <link> first, common paths as fallback), verification via real fetch before saving, and collision handling with '-2' suffix. It does not mention rate limits or failure modes, but the key behavioral traits are articulated clearly.

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?

The description is well-structured with a concise lead sentence, a bulleted Args list, and a Returns line, front-loading the core behavior. It could be slightly tighter, but every sentence provides meaningful information without fluff.

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

Completeness5/5

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

For a tool with no annotations, no output schema, and minimal input schema, the description covers all crucial aspects: input constraints, default behaviors, return values, and the underlying fetch/verify workflow. It even documents the exact return tuple, making this sufficient for an agent to use it correctly.

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

Parameters5/5

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

The Args section in the description adds substantial meaning beyond the schema: it specifies URL format, name constraints (lowercase/digits/hyphens, 2-40 chars, empty→domain-derived, collision suffix), note as one-liner, tag vocabulary, and picked_by as free-form provenance. The schema itself only provides 'string' type and default empty strings, so the description is essential and complete.

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 clearly states 'Add a source to the shelf' and explains the feed discovery behavior, making the tool's function specific. It doesn't explicitly reference sibling tools like shelf_remove or shelf_suggest, so differentiation is implied through the 'add' verb rather than named alternatives.

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

Usage Guidelines2/5

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

No guidance is provided on when to choose this tool over siblings like shelf_suggest or shelf_remove. The description explains what the tool does but lacks explicit when-to-use or when-not-to-use context, leaving the agent to infer based on name alone.

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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