wm_predict

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

With no annotations, the description carries the full burden and fails to disclose whether this is a read-only operation, whether it requires a prior state, or what the output format is. The term 'predict' implies a non-mutating operation, but that is not explicitly stated.

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 very terse (5 words), which makes it front-loaded but under-specified for a tool with a nested action object. It is not overly verbose, but the brevity sacrifices necessary clarity.

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 the complexity of the input (nested action, steps parameter) and the absence of an output schema, the description is incomplete. It does not explain return values, side effects, or how the prediction relates to the current SNN state, leaving significant gaps for an AI agent.

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 input schema provides descriptions for both parameters ('steps' and 'action') with 100% coverage. The tool description adds no additional meaning beyond the schema, so it stays at the baseline for schema-heavy tools.

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 tool as 'Predict Next SNN State in Latent Space', giving a specific verb and resource. However, it does not mention that the prediction is conditioned on an injected action, nor does it distinguish itself from similar tools like snn_step or get_snn_state beyond the word 'predict'.

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 usage guidance is provided. The description does not state when to use this tool instead of alternatives, nor does it mention prerequisites such as an existing encoded state or a preceding simulation step.

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