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

No annotations are present, so the description carries the full behavioral burden. It does add useful non-schema context — the required 'boosts:read' scope and the most-recent-first ordering. However it does not describe retention, what a 'run status' can be, or pagination/failure behavior, leaving gaps for a polling 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?

A single sentence that front-loads the verb, resource, ordering, and purpose with zero filler. 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?

For a simple read-only list with a fully documented schema, the description covers purpose, ordering, use case, and auth scope. With no output schema, it only loosely signals the return ('their status'), which is a minor gap but not blocking.

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% (limit, offset, site_id all documented in the schema), so the baseline is 3. The description adds no parameter-level detail beyond what the schema already provides.

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 ('List the inline warm runs of a site'), plus the ordering (most recent first) and the downstream purpose (polling after warm_site). The qualifier 'warm' and the reference to warm_site distinguish it from the sibling list_runs, though the contrast is implicit rather than stated.

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?

Explicitly frames the use case: poll warm run status after calling warm_site. That gives a clear when-to-use context. It stops short of naming alternatives (e.g., list_runs) or stating when not to use it.

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