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

Annotations already declare readOnly and idempotent behavior, but the description adds important behavioral context beyond that: data is aggregated over a 28-day window, comes from real connections rather than self-reported sources, and missing countries are absent rather than reported as zero. It also explains the call-quality verdict is derived from latency and jitter rather than bandwidth, which clarifies a potentially surprising output.

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 composed of four dense sentences, each covering a distinct useful aspect: metric definitions, data provenance, geographic coverage, and missing-data semantics. It is front-loaded with the central purpose and contains no redundant or filler content.

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 read-only comparison tool with rich schema descriptions and an output schema, the description covers the key unknowns: what is measured, from what source, over what period, how missing data behaves, and why call quality is not simply bandwidth. An agent has all information needed to select and invoke the tool 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?

Schema description coverage is 100%, with parameter descriptions already explaining sort directions, units, and filter thresholds. The description adds broader context about the measured metrics but does not add further meaning to individual parameters beyond what the schema already provides, so the baseline score of 3 applies.

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 the tool measures connection quality per country from Cloudflare Radar, listing specific metrics: median download/upload, latency/jitter, and a call-quality verdict. It distinguishes itself from sibling comparison tools by focusing specifically on internet connectivity rather than climate, healthcare, or cost.

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 provides clear context on when the tool is useful, noting coverage extends beyond the site's country pages and that it works for places with no dedicated page. However, it does not explicitly name sibling alternatives or state conditions when another tool would be more appropriate, so it stops short of a full usage decision rule.

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