cuba_hipotesis

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

Since no annotations are provided, the description carries full burden. It explicitly states 'Read-only', describes the ranking formula (path_strength × importance), and mentions traversing causal relations backwards. This fully discloses the tool's behavior.

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 two sentences, front-loaded with the core purpose. Every word adds value, no redundancy or fluff.

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 an inference tool with 4 parameters and no output schema, the description is complete. It explains what the tool does, how results are ranked, and that it is read-only. No missing information critically needed for invocation.

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%, but the description adds significant context: explaining abductive inference, the ranking method, and the read-only nature. This enhances understanding of each parameter's role beyond their individual descriptions.

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 specifies 'abductive inference' with a clear verb+resource: given an observed effect, find plausible causes by traversing causal relations backwards. It distinguishes from siblings by focusing on hypothesis generation. No other sibling tool suggests this abductive role.

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 clearly states when to use: when you have an observed effect and need plausible causes. It does not explicitly state when not to use or mention alternatives, but the context is clear enough for an agent to decide.

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