rss_parse

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

With no annotations, the description fully covers behavior: it discloses error conditions (unreachable URL, invalid RSS/Atom XML) and explicitly states 'has no side effects'. This is thorough for a read-only tool.

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 concise with three sentences, each adding distinct value: main action, error/side effects, and use cases. Slightly verbose in listing output fields, but still 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?

Given no output schema, the description adequately explains the return structure (feed metadata and items with fields). Together with the input schema, it provides complete context for a two-parameter tool.

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% but the description adds value by clarifying default and max for 'maxItems' (20, max 200) and describing the output structure (feed metadata, items with specific fields).

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 states it fetches and parses RSS 2.0 or Atom 1.0 feeds and returns structured JSON. It distinguishes from sibling tools like 'fetch_structured' and 'fetch_html' by specifying the exact feed formats.

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 advises using this tool instead of fetching raw XML to save tokens, and lists ideal use cases (news aggregation, content monitoring). It lacks explicit when-not-to-use or alternatives, but the context is clear.

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