ASTRA

tcai_workspace_state

Read-onlyIdempotent

Retrieve global neuronal workspace metrics—ignition, focus, qualia, sync R, unity, and access history—to assess consciousness proxies in ASTRA simulations.

Instructions

Global Neuronal Workspace state: ignition, focus, qualia, sync R, unity metrics, access history

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": "tcai_workspace_state"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.1.0Sep 23, 2026

TDQS

C2.8/5.0

Behavior2/5

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

The annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered structurally. The description adds no behavioral context beyond that — nothing about whether the workspace state is live or cached, how access history accumulates, or how frequently it changes. Opaque terms like 'sync R' and 'unity metrics' are offered without explanation.

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?

It is a single short fragment with no padding, which is efficient. But it is a bare colon-delimited field list rather than a front-loaded statement of purpose, so the reader must infer the verb and the tool's role.

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

Completeness3/5

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

With no output schema and no parameters, the description is the only window into the return value, and it names six distinct metric groups. That is some coverage, but the names are jargon ('ignition', 'qualia', 'sync R', 'unity metrics') with no explanation of what they mean or their units, which leaves the agent unable to interpret results.

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 tool takes zero parameters, so the baseline is 4 under the rubric. There is no argument surface for the description to clarify or omit.

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 names a specific resource (Global Neuronal Workspace state) and enumerates the facets it exposes (ignition, focus, qualia, sync R, unity metrics, access history), so an agent can infer it reads state. However, it is a noun-phrase field list with no verb and no differentiation from siblings like tcai_metrics, get_snn_state, or wm_status, which plausibly report overlapping state.

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 indication of when to call this versus tcai_metrics, tcai_self_model, tcai_second_order, or the other tcai_* state readers. No prerequisites, no trigger conditions, and no exclusions are stated, leaving routing entirely to the agent's guess.

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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