sesgo_cluster

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

Without annotations, the description carries full burden. It discloses the tool returns two values (bias and confidence) and adds behavioral context via the interpretation rule. No destructive actions mentioned, but the tool appears read-only.

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 concise (two sentences plus a rule) and front-loaded with the main action. Every sentence adds value, though the rule could be integrated more efficiently.

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?

Given no output schema, the description explains return values (bias percentage and confidence reading) and provides context for interpretation. Sufficient for the tool's simple use case.

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?

The schema already describes the single parameter 'cluster' with allowed values. The description adds no additional meaning about the parameter beyond its return values, so 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?

The description clearly states the tool returns the 'Vinícius bias' (percentage of its tokens) and confidence reading, with specific verbs and resource. It distinguishes from siblings like 'auditar_cluster' by focusing on bias metric.

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 provides a rule for interpreting high percentages (treat as hypothesis) and specific guidance for clusters G/H/I (cross-check). However, it does not explicitly state when to use this tool vs alternatives, leaving usage context implied.

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