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christophejlegros-lgtm

ASTRA — Unified Research Lab + MCP Server

wm_train_step

Train an online world model by injecting currents into selected neurons with defined strengths and duration.

Instructions

Online World Model Training Step

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction applied between observations
Behavior1/5

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

No annotations are provided, so the description carries full burden. It does not disclose whether the tool modifies state, requires a specific world model state, or is safe to call multiple times. The description is too brief.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single vague phrase. While concise, it is under-specified and fails to convey necessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the nested parameter, no output schema, and many sibling tools, the description is completely inadequate. It provides no explanation of what the training step accomplishes, what the response looks like, or how it fits with other tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description does not add any additional context about the 'action' parameter beyond the schema's own descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states it's a training step for the world model, but is vague. It does not specify that it applies an action and updates the model, nor does it distinguish from sibling tools like wm_encode or wm_predict.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs. alternatives. The description does not mention prerequisites or context for training.

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

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