ASTRA MCP Server

wm_predict

Forecast future SNN states in latent space from a spike injection action. Set target neurons, strengths, and rollout steps to simulate the network's next state.

Instructions

Predict Next SNN State in Latent Space

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_predict"
  }
}

Input Schema

Name	Required	Description	Default
`steps`	No	Number of prediction steps (rollout)
`action`	Yes	Spike injection action to condition prediction on

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.0Sep 6, 2026

TDQS

C2.8/5.0

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 burden of behavioral disclosure. It only states a prediction outcome and gives no information about side effects, whether the action injection mutates state, determinism, or what is returned. This is a significant gap for a tool with no safety annotations.

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

Conciseness3/5

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

The description is short, front-loaded, and free of filler, which is good. However, it is under-specified for a tool with a nested action schema, and the brevity comes at the cost of useful detail.

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

Completeness2/5

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

With no annotations and no output schema, the description leaves critical details unstated: what the tool returns, how the action conditions the prediction, and how steps affects the rollout. An agent would have to rely heavily on the schema and experimentation to use this tool 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 schema already documents the purpose of steps, action, targetNeurons, strengths, and duration. The description adds no parameter-level meaning, so the baseline score of 3 applies.

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

Purpose4/5

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

The description uses a specific verb ('Predict') and names a concrete resource ('Next SNN State in Latent Space'), which clearly communicates the tool's core function. It does not explicitly differentiate it from siblings like wm_plan or wm_train_step, but the core purpose is still identifiable.

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?

There is no guidance on when to use this tool versus alternatives such as wm_plan, snn_step, or wm_encode. The description provides no context, prerequisites, or exclusions to help an agent choose this tool over its siblings.

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