tcai_active_inference

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It describes the output data but does not indicate whether the tool is read-only, has side effects, requires permissions, or has rate limits. The statement about being 'the principled quantity' suggests importance but not behavioral traits.

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

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

The description is a single dense sentence with front-loaded key information and minimal redundancy. The inclusion of academic references (Da Costa et al. 2020; Legros 2026 §4.3) adds credibility but slightly reduces conciseness for an AI agent.

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

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

Given the complexity of active inference and no output schema, the description lists all returned quantities comprehensively. However, it lacks details on output format, usage context, and behavioral notes, making it adequate but not fully complete.

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

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

The input schema has no parameters, so schema coverage is effectively 100%. The description adds no parameter information because none exist. Baseline score of 3 is appropriate.

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

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

The description explicitly states the tool provides 'Active-inference core telemetry (v2.9)' and enumerates specific quantities (variational free energy F, expected free energy G(π) decomposed, etc.), clearly distinguishing it from sibling tools like tcai_convergence or tcai_metrics by emphasizing it's 'the principled quantity the halting criterion thresholds on — not a heuristic correlate'.

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

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

The description implies usage for active inference telemetry and halting criterion thresholds, but does not explicitly state when to use or avoid this tool compared to siblings like tcai_convergence or tcai_metrics. No exclusions or alternative recommendations are provided.

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