tcai_second_order

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

With no annotations provided, the description carries the full burden of behavioral disclosure. The word 'snapshot' strongly suggests a non-mutating read operation, and 'observing' reinforces this. However, it does not explicitly rule out side effects, state changes, or prerequisites, nor does it explain what 'self-evidencing' means behaviorally. The description adds some context (the system observing/correcting its predictive capacity) but lacks explicit safety or side-effect declarations.

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 concise, consisting of two sentences. It front-loads the tool's purpose ('Second-order loop snapshot') and then lists the contained elements. The second sentence adds conceptual context, though the citation ('Legros 2026 §3.2') may be of limited value to an AI agent. Every word earns its place, but the dense jargon might reduce immediate readability.

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 tool has no input schema parameters and no output schema, the description must explain both purpose and return semantics. It successfully lists the snapshot components but does not describe the output format (e.g., JSON shape, numeric ranges, or units). It also omits prerequisites or whether this is a lightweight read or a costly computation. The composite nature justifies a higher complexity, so the description is adequate but not 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 tool has zero parameters, so the baseline for this dimension is 4. The description adds no parameter information because none exists, and the schema is trivially complete (100% coverage). There is no need for compensation; the baseline applies.

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 clearly identifies the tool as a 'second-order (self-evidencing) loop snapshot' and enumerates the specific state components it captures (meta-learning velocity, RND curiosity, capability model, meta-consciousness score, developmental stage). This distinguishes it from more focused siblings like tcai_meta_learning or tcai_curiosity by its composite nature, though the absence of an explicit verb (e.g., 'get', 'retrieve') and reliance on jargon slightly reduce clarity.

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?

No explicit guidance is given about when to use this tool versus alternatives. While the description implies it provides a high-level overview of multiple self-monitoring aspects, it does not state conditions, exclusions, or mention sibling tools that might be more appropriate for specific details (e.g., 'use tcai_meta_learning for in-depth meta-learning metrics'). The usage context is only implicitly conveyed.

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