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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior, so the safety profile is covered. The description adds genuinely new behavioral context absent from structured fields: the per-call cost of $0.002 USDC via x402, which matters for an agent deciding whether to invoke it.

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?

Two short sentences, zero filler, with the purpose front-loaded and the cost constraint following. Every 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?

An output schema exists, so return values need not be explained. For a zero-required-parameter, single-optional-parameter feed, purpose plus cost is nearly complete; only routing guidance relative to siblings is missing.

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% for the single 'limit' parameter (max 50, default 25), so the schema fully documents it. The description adds no additional semantics about the limit, which matches the baseline of 3 when structured data carries the burden.

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 with a scoping qualifier: 'newest arXiv submissions in cs.LG (Machine Learning).' The category qualifier implicitly distinguishes it from siblings such as arxiv__recent-cs-ai and arxiv__recent-stat-ml, though it never explicitly contrasts with the generic arxiv__recent feed.

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 says what the tool returns but gives no guidance on when to choose it over the many sibling category feeds or the arxiv__search tool. It states no prerequisites, exclusions, or alternatives, leaving routing entirely to the agent's inference.

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