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

Annotations already signal read-only and open-world behavior, lowering the burden. The description adds valuable non-obvious behavior: sources are highly uneven in size, limit is per-source, and hitting exactly limit rows likely means more data exists. This goes well beyond the schema and does not contradict the annotations.

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?

The description is dense yet compact: two sentences cover purpose, parameter semantics, and a data-shape heuristic. Code-formatting for parameter names improves scanability, and every sentence adds value.

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 only two optional parameters, an output schema present, and read-only/open-world annotations, the description covers invocation choices and important size caveats. It could add an explicit use case or relationship to a specific sibling tool, but the essentials for selecting and calling this tool are present.

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 0%, so the description must compensate, and it does. It explains that omitting source means all sources and that source accepts slugs from rankings/signals, and it defines limit as a per-source cap. It does not state the default limit value, but that is already present in the schema.

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 clearly identifies the resource as per-source consensus snapshots and enumerates seven distinct source types, making the tool's purpose recognizable. It lacks an explicit verb and does not contrast with sibling tools, but 'consensus' is a distinct resource among the rayana_ai_* siblings.

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 description gives explicit invocation guidance: omit source to query all sources, or pass a slug returned by rankings/signals to target one source. It also provides a practical heuristic about uneven source sizes and how to read more deeply with less context, though it does not name sibling alternatives or exclusions.

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