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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds real behavioral context beyond that: it explains what 'runsCited 0' means and explicitly warns that byEngine is the whole window, not a rate inside a single category — a non-obvious interpretive rule. It omits auth, rate limits, or freshness of the window, keeping it from a 5.

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 core return contract before the usage examples and caveats, and every clause carries information. It is somewhat dense as one long paragraph, which makes it slightly harder to scan than a bulleted breakdown.

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?

No output schema exists, so the description must carry the return shape — and it does: overall rate, per-engine cited/observed runs and evidence-floor flag, confidence tier, ranks overall and by source role, and per-category segments. Combined with the byEngine caveat, an agent has everything needed to call and interpret 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?

Schema coverage is 100% and the single 'domain' parameter is fully documented in the schema, including that a URL is accepted. The description restates 'one domain or URL' without adding format, normalization, or error-handling detail beyond what the schema provides, so the baseline 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?

States a specific verb and resource: for one domain or URL, returns how often AI answer engines cite it, then enumerates the returned measures. This clearly separates it from siblings like mri_get_cited_sources (which lists sources) and mri_get_engine (engine-level data).

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?

Gives concrete triggering questions ('does ChatGPT cite <site>', 'what share of Perplexity runs cite <site>'), which is strong context for selecting this tool. It stops short of naming an alternative tool for adjacent questions, so routing between mri_get_* siblings still requires inference.

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