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

Annotations already declare readOnlyHint, idempotentHint and non-destructive, so the safety profile is covered. The description adds real behavioral value beyond that: the auth requirement (_apiKey), and the fact that the bar is computed as ceil(5% of tracked podcasts) and rises as the corpus grows — non-obvious, load-bearing context for interpreting results.

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?

Front-loads the return payload, then the formula and auth note, then an example call. Slightly dense with the em-dash enumeration, but every sentence earns its place — no redundant restatement of the title.

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 carries the burden of describing returns, and it does so at a high level (counts and threshold). It does not specify exact field names or shape, but for a zero-argument stats endpoint this is nearly complete.

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 coverage is 100% and the single parameter is optional, so the schema baseline already applies. The description still adds meaning by stating the key is required and must be passed via _apiKey, confirming auth behavior not obvious from an optional-looking schema field.

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 (call/stats) and resource (CanonCannon corpus) and enumerates exactly what it returns: entry, episode, idea, person counts, tracked-podcast count, and the independent-podcast threshold. This is clearly distinguishable from siblings like canon_get or canon_search, which fetch entities rather than corpus-level metrics.

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?

"Call this to state the standard you are quoting" gives a concrete usage context (grounding a claim against the canon bar). It stops short of naming an alternative or an explicit when-not condition, so it is clear but not fully routed.

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