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christophejlegros-lgtm

ASTRA Unified Research Lab MCP Server

tcai_cycle

Run one or more consciousness cycles simulating SNN signals, emotion, and self-modeling, with optional early halt when the second-order loop reports sustained satisfaction.

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]
narrativeNoAnnotation for the memory record
maxSurpriseNoHalt threshold: normalized WM surprise ≤ (default 0.2)
maxCuriosityNoHalt threshold: intrinsic reward ≤ (default 0.2)
minStabilityNoHalt threshold: development stability ≥ (default 0.7)
rewardSignalNoTask feedback ∈ [−1,1]
controllabilityNo
predictionErrorNoWorld-model surprise (raw)
stopWhenSatisfiedNoHalt early when the second-order loop reports sustained satisfaction
minLearningVelocityNoHalt threshold: learning velocity ≥ (default 0.5)
predictionConfidenceNo
satisfactionPatienceNoConsecutive satisfied cycles required to halt (default 3)
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral disclosure burden. It does explain the cycle pipeline and the early-halt behavior via stopWhenSatisfied, but it does not disclose side effects (e.g., state mutation, memory writes) or what the tool returns. This is minimally adequate but incomplete.

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?

Two sentences, front-loaded with the purpose. The pipeline enumeration is dense but informative, and the second sentence provides actionable halt criteria. Every sentence earns its place with no fluff.

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?

For a 14-parameter simulation loop with no output schema, the description gives the internal pipeline and halt conditions but omits expected output/return values, prerequisite system state, and how this relates to sibling cycle tools. The rich schema partially compensates, making it minimally viable but not fully complete.

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 79%, so the schema already documents most parameters. The description adds useful context for stopWhenSatisfied and the cycle stages, but it does not compensate for the three undocumented parameters (threat, controllability, predictionConfidence). Baseline 3 is appropriate.

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?

The description opens with 'Run one or more ACM cycles' — a specific verb plus resource. It names the port and lists the internal pipeline, making its purpose unmistakable and differentiating it from sibling state-read or single-step tools like snn_step and tcai_second_order.

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 snn_step, tcai_second_order, or wm_*. The only usage hint is the stopWhenSatisfied explanation, which is parameter-level guidance rather than tool-selection guidance.

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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