tcai_second_order

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

With no annotations, the description must fully disclose behavior. Calling it a 'snapshot' suggests a read-only operation, but the phrase 'observing and correcting its own predictive capacity' introduces ambiguity about potential side effects. It does not clarify whether the tool modifies state, requires permissions, or what the return format is, leaving key behavioral traits undisclosed.

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 compact and front-loaded, with the first sentence listing the tool's purpose and contents. The second sentence adds theoretical context that is somewhat extraneous but not bloated. Every sentence contributes to understanding, though the reference to 'Legros 2026' is jargon-heavy.

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?

The description provides a reasonable overview of the output components and positions the tool as a meta-cognitive snapshot, which is helpful given the domain's complexity. However, it lacks details on value semantics, units, or how to interpret the combined snapshot, and it does not clearly state whether the operation is purely observational. Without an output schema, more explanatory content would improve completeness.

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 schema is empty and there is nothing to describe. The baseline of 4 applies, and the description correctly adds no irrelevant parameter details.

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 lists the specific components it covers (meta-learning velocity, RND curiosity, capability model, meta-consciousness score, developmental stage), which distinguishes it from sibling tools that focus on individual aspects. However, it lacks an explicit verb like 'get' or 'retrieve', leaving the action somewhat implicit.

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 use for obtaining a combined view of self-monitoring metrics, but it does not explicitly state when to use this tool instead of individual sibling tools like tcai_metrics or tcai_self_model. No exclusions or alternative comparisons are provided, so the usage context is only implicit.

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