cuba_hipotesis

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

No annotations are provided, so the description carries the burden. It does disclose a key behavioral trait, 'Read-only', which is valuable safety context. However, it omits the backward-traversal/ranking behavior, depth limits, and result-count capping that shape what the agent gets back.

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 short sentences with zero waste; the core purpose leads and the read-only qualifier follows. Nothing needs trimming.

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?

For a read-only inference tool with no output schema and no annotations, the description covers purpose and safety but says nothing about the shape of the returned hypotheses (ranking, count, depth) or about the backward traversal mode already documented only in the schema.

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%, so the schema already documents action, effect, limit, and max_depth with defaults and the ranking formula. The description adds no parameter meaning beyond that, 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?

States a specific verb+resource: 'find plausible causes of an observed effect', and names the inference type (abductive). An agent can tell it produces candidate causes rather than a stored hypothesis (contrast with the memory_hipotesis sibling), though it never names that sibling explicitly.

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?

Usage is implied by 'observed effect' input, but there is no explicit when-to-use, when-not-to-use, or guidance on choosing this over memory_hipotesis or cuba_reflexion/cuba_contradiccion.

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