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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new operational context: the call is fully public and free, requires no key, and returns the same figures published on atticstandard.com. That auth/tier disclosure is exactly the kind of context annotations cannot convey.

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 sentences, front-loaded with the deliverable and its six components, then the value framing. No filler. It is dense with internal jargon ('frontier context ceiling', 'training cutoff lag'), but each clause earns its place by defining the payload.

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?

With no output schema, the description takes on the burden of explaining what comes back, and it does so by enumerating all six measures. Zero required parameters, read-only annotations, and a stated free tier leave nothing an agent needs missing before invoking it.

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?

There is one optional parameter with 100% schema description coverage, so the schema already explains _atom_api_key and its free-tier omission. The description corroborates the free/public nature but adds no syntax or format detail beyond the schema. Baseline 4 applies when the schema carries the parameter semantics and the parameter is optional.

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 the resource precisely: six enumerated capability/coverage measures (reasoning tier share, long-context saturation, frontier ceiling, output ceiling spread, training cutoff lag, vendor modality breadth). That is specific enough for an agent to know what it gets. It only gestures at sibling differentiation by referencing 'the market KPIs' rather than naming get_kpis or get_model_detail, so it stops short of a 5.

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 gives interpretive context ('read alongside the market KPIs, they explain why models are priced the way they are'), which implies a use case. However it never states when to prefer this over get_kpis, get_market_stats, or get_model_detail, and offers no exclusions or prerequisites. Usage is implied, not directed.

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