ASTRA Unified Research Lab MCP Server

wm_surprise

Detect violation-of-expectation in neuromorphic simulations by injecting specified strengths into target neurons and observing response deviations.

Instructions

Violation-of-Expectation Detection

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "action": "<action>"
    },
    "name": "wm_surprise"
  }
}

Input Schema

Name	Required	Description	Default
`action`	Yes	Action that was applied

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.0.0Aug 17, 2026

TDQS

C2.3/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full behavioral disclosure burden. It only says "Detection," which weakly implies a read-like operation, but it never states side effects, internal state requirements, how the action input is applied, or what the result represents.

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 and front-loaded, but this is under-specification rather than effective conciseness. The three-word label does not provide enough substance to earn a higher score.

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?

The tool has a nested required parameter, no output schema, and no annotations, making rich contextual guidance essential. The description explains neither the expected behavior, the return value, nor how the action input relates to surprise detection, leaving the agent without enough information to invoke it correctly.

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 description coverage is 100%, so the nested action object, targetNeurons, strengths, and duration are already documented in the schema. The description adds no parameter-level meaning, but it does not need to because the schema already covers the fields.

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?

"Violation-of-Expectation Detection" names a recognizable cognitive-science concept and implies the tool detects mismatches between predictions and observations. However, it is a noun phrase rather than a specific verb-plus-resource statement, and it does not explicitly distinguish wm_surprise from closely related siblings like wm_predict or wm_train_step.

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?

The description gives no guidance on when to use this tool instead of wm_predict, wm_train_step, or any other sibling. There is no mention of prerequisites, intended workflow, or exclusions, so an agent must infer usage from the name and schema.

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

Deploy Server