ASTRA MCP Server

wm_encode

Encodes spiking neural network states into a latent space, enabling compact representation and further analysis within neuromorphic simulation workflows.

Instructions

Encode SNN State to Latent Space

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "wm_encode"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.0Sep 6, 2026

TDQS

B3/5.0

Behavior2/5

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

With no annotations, the description must carry the full burden of disclosing side effects and return behavior, but it only states the intended transformation. It does not say whether the SNN state is modified, whether a latent representation is returned, or whether any prerequisites exist, which matters for an unannotated tool.

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

Conciseness4/5

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

The description is a single front-loaded sentence with no filler, and every word contributes to the operation. It is compact, though arguably too short to cover behavior; that tradeoff is better scored under contextual completeness.

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?

There is no output schema and no annotation coverage, so the description should clarify what the agent receives from the encode operation and whether the operation has side effects. It does neither, leaving an agent to guess at the meaning and return value of 'latent space' in this system.

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

Parameters4/5

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

The input schema has zero properties and schema description coverage is 100%, so the parameter baseline is 4. The description adds no parameter-level details, but none are needed because there are no parameters to describe.

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 names a specific verb ('Encode'), a resource ('SNN State'), and a target ('Latent Space'), so it is not a tautology and reads as a distinct transformation operation compared to siblings like get_snn_state or wm_predict. However, it relies on the term 'Latent Space' without explaining what the output represents, so the purpose is clear but not fully specified.

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 about when to call this tool rather than get_snn_state, wm_predict, or the other wm_* siblings, and no exclusions or alternative conditions. The only clue is the verb 'encode', which implies a use case but leaves the decision entirely to inference.

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