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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behaviorally useful context beyond the annotations: the 20-match cap, no-account-needed requirement, canonical URL semantics, and that the search covers every public dataset. This exceeds the baseline expected when annotations are present.

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?

Every sentence earns its place: the first states the action and output, the second covers authentication and gives an example, and the third explains downstream chaining and sibling differentiation. The most important information is front-loaded and there is no 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?

For a simple one-parameter search tool with no output schema, the description fully specifies the return format, the limit, the auth expectation, the id/url semantics, the linkage to fetch, and the alternative sibling tool. There is no critical missing information that would prevent an agent from calling it correctly.

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?

The schema has 0% description coverage, so the description must compensate. It provides a concrete example query ('hourly airport weather observations') and the expected overall request shape, which makes the single string parameter's purpose clear. It doesn't elaborate on edge cases such as empty results or term handling, but for a single free-text query the compensation is strong.

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 names a specific verb and resource ('Search every public dataset on Mostly Right'), states the exact return shape ({results: [{id, title, url}]}), and explicitly distinguishes itself from the sibling search_datasets. An agent can immediately tell what this tool does and how it differs from the richer paged variant.

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?

It explicitly positions this as the 'plain search-and-fetch pair' versus 'search_datasets [as] the richer, paged version with topics, publishers and summaries,' giving a clear selection criterion. It also tells the agent to pass a result's id to fetch for the full dataset document, which is concrete downstream usage guidance.

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