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christophejlegros-lgtm

ASTRA Unified Research Lab MCP Server

tcai_cycle

Run ACM cognitive cycles simulating the full consciousness loop from SNN signals to self-model; optionally halt early when sustained satisfaction is reached.

Instructions

Run one or more ACM cycles (the_consciousness_ai port): SNN signals → AKOrN binding → GNW ignition → qualia → emotion → reward shaping → emotional memory → self-model → second-order loop. Set stopWhenSatisfied to halt early once the recursive loop reaches a sustained satisfactory (converged, low-curiosity, stable) regime.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cyclesNoNumber of cycles (default 1; upper bound if stopWhenSatisfied)
threatNo
noveltyNoSurprise/curiosity ∈ [0,1]
setpointNoContinuous controller substrate setpoint to regulate toward (v2.9, default 0.3)
narrativeNoAnnotation for the memory record
closedLoopNoEnable closed-loop actuation (AIF action drives the substrate); default on
maxEpistemicNoHalt threshold: expected info gain ≤ (default 0.1)
rewardSignalNoTask feedback ∈ [−1,1]
epsFreeEnergyNoHalt threshold: absolute |ΔF| ≤ (nats, default 0.02)
relFreeEnergyNoHalt threshold: |ΔF| ≤ rel·F, scale-free (default 0.03)
minTaskQualityNoHalt threshold: realized task quality ≥ (default 0.6)
productionLoopNoClose the loop through the shared production SNN (read+write); default off (v2.9)
controllabilityNo
predictionErrorNoWorld-model surprise (raw)
stopWhenSatisfiedNoHalt early when the second-order loop reports sustained satisfaction
predictionConfidenceNo
satisfactionPatienceNoConsecutive satisfied cycles required to halt (default 3)
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the internal processing pipeline and the early-halting condition, which adds useful behavioral context. However, it does not explicitly state whether the cycle mutates external state (e.g., writes emotional memory or updates the self-model) or what side effects the agent should expect.

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

Conciseness5/5

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

The description is two sentences, front-loaded with the core action, then provides the pipeline flow and the key stopping parameter. Every sentence earns its place without redundancy.

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?

The tool is complex with 17 parameters and no output schema, and the description gives a good high-level explanation but does not mention what the tool returns (e.g., metrics, satisfaction state, or status). Given the schema's strong parameter coverage, the description is minimally viable but leaves output behavior unspecified.

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?

Schema description coverage is 82%, so the baseline is 3. The description adds meaningful semantics for stopWhenSatisfied, explaining that it halts early on a sustained satisfactory regime. This goes beyond the schema's basic property description, earning a 4.

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

Purpose5/5

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

Description uses a specific verb ('Run') and resource ('ACM cycles') and clearly differentiates the tool by listing the full pipeline stages (SNN signals, AKOrN binding, GNW ignition, etc.), which distinguishes it from sibling tools like tcai_second_order or orch_cycle.

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

Usage Guidelines3/5

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

The description gives context for the operation and explains when to set stopWhenSatisfied, but it does not explicitly state when to prefer this tool over alternatives such as tcai_second_order or orch_cycle. Usage guidance is implied rather than explicit.

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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