wm_encode

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

With no annotations, the description must fully disclose behavior. It only states the action (encode) without specifying side effects, data requirements, or whether the operation is read-only or potentially destructive. This is insufficient for an AI agent to understand 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 a single, concise sentence with no extraneous information. It is front-loaded and efficient, earning the highest score for conciseness.

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 zero parameters, no output schema, and no annotations, the description is minimal. It does not explain what the latent space is, how encoding works, or what the output represents. For a tool involved in SNN state processing, this leaves significant gaps in understanding.

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 tool has zero parameters, so the schema coverage is 100% (empty schema). Per guidelines, 0 parameters warrants a baseline of 4. The description adds purpose (encoding to latent space) beyond the schema, but since there are no parameters, this dimension is inherently high-scoring.

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 'Encode SNN State to Latent Space' clearly identifies a specific verb (encode) and resource (SNN state) with a target (latent space). It is distinct from vague siblings like 'wm_status', but lacks differentiation from 'wm_predict' which may involve similar operations, so it is not a 5.

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 guidelines are provided. The description does not indicate when to use this tool versus alternatives like wm_predict or wm_train_step, nor does it mention prerequisites or context.

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