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

Annotations already declare this a read-only, idempotent, closed-world operation, so the safety profile is covered. The description adds genuinely useful behavioral context that annotations cannot: the metric semantics (citation rate as share of monitored answer runs, rank, percentile) and the full source-role taxonomy, which is expanded beyond the schema's example list.

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 core purpose before the usage examples, and no filler. The first sentence is dense with enumerated fields but each clause earns its place by describing what is returned.

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 does the work of describing return values (domains, citation rate, rank, percentile, source role), which is exactly what an agent needs to read results. Remaining gaps are freshness/date range of the monitored runs and how limit/offset pagination behaves across the ranked list.

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 60%, and the description compensates by enumerating source_role values that the schema only hints at (adding academic/government, market database and wire distribution). question_shape and category meanings are aligned with the schema; limit/offset are left to the schema, which is acceptable since they carry defaults.

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 first sentence names a specific resource (ranked source domains cited by AI answer engines) scoped to one category and question shape, and enumerates the returned measures. This is clearly distinct from siblings like mri_get_category, mri_get_domain and mri_list_categories, which cover categories and single domains rather than cited-source rankings.

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?

The second sentence gives two concrete use cases ('which publications does ChatGPT or Perplexity cite for <category>' and 'where should a brand earn coverage to be cited'), which tells the agent when to reach for this tool. It stops short of naming alternatives or stating when NOT to use it (e.g., for a single domain, use mri_get_domain).

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