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christophejlegros-lgtm

ASTRA — Unified Research Lab + MCP Server

wm_surprise

Detects violation-of-expectation signals by applying targeted neuronal injections and analyzing resulting network deviations.

Instructions

Violation-of-Expectation Detection

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction that was applied
Behavior1/5

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

With no annotations and only a four-word description, the tool gives zero insight into its behavioral traits. There is no disclosure of whether it is read-only, destructive, requires permissions, or has side effects. The agent cannot infer safety or expected impact.

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 extremely short (4 words) but not effectively concise. It lacks structure and fails to convey necessary information. A concise description would be informative yet brief; this one is under-specified.

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 complexity of the input schema (nested object), lack of annotations, and absence of output schema, the description is completely inadequate. The tool has no explanation of what the detection produces (e.g., a metric, a signal, an event) and no context about its role in the working memory ecosystem.

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

Parameters2/5

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

While the schema has 100% coverage with descriptions for each property, the tool description adds no additional meaning. The description does not explain how the action parameters (targetNeurons, strengths, duration) relate to violation-of-expectation detection. The semantic connection between parameters and tool purpose is missing, leaving the agent without critical context to interpret parameter values.

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

Purpose2/5

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

The description 'Violation-of-Expectation Detection' states a concept but lacks a specific verb and resource. It does not clarify whether this tool computes a surprise score, triggers an event, or returns data. It fails to distinguish from sibling tools like wm_predict or wm_plan.

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

Usage Guidelines1/5

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

No usage guidelines are provided. There is no indication of when to use this tool versus alternatives, no prerequisites, and no mention of context where it should be avoided.

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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