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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'compute,' which hints at a pure function, but it does not mention side effects, state requirements, dependencies between parameters, or any performance or access considerations. This is insufficient for a tool with no annotation fallback.

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 a single efficient sentence that front-loads the verb and resource. It avoids fluff, though it sacrifices useful detail for brevity. As conciseness, it is strong; as structure, it lacks any breakdown or headings, but that is acceptable for such a short text.

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?

The tool has 6 parameters, nested objects, an output schema, and no annotations. A one-line description does not explain how inputs relate to outputs, what the output schema represents, or when to provide optional parameters like skill_name or reachable_states. The description is markedly incomplete for the complexity of the tool.

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 for parameter meaning. It mentions high-level concepts (novelty, empowerment, learning progress) but does not map them to actual_state, predicted_state, reachable_states, or other parameters. The agent cannot determine how to populate the arguments from this text alone.

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 states a specific verb ('Compute') and resource ('intrinsic drives'), then enumerates the exact components (novelty, prediction surprise, empowerment, learning progress). This is unambiguous and distinguishes the tool from generic compute tools, though it does not explicitly contrast with 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?

No guidance is provided about when to use this tool versus alternatives such as compute_intrinsic_rewards or other cognitive compute functions. The description only states what it does, leaving the agent to infer applicability with no context or exclusions.

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