tcai_second_order

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

With no annotations, the description must fully disclose behavioral traits. The term 'snapshot' suggests a read-only operation, but the phrase 'observing and correcting its own predictive capacity' introduces ambiguity—whether the tool also performs corrections or only reports. No explicit statement of non-destructiveness or side effects is given.

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 two sentences, front-loading the purpose with a list of components. It is concise, though the citation '(Legros 2026 §3.2)' may be non-actionable for an AI agent. Still, minimal waste overall.

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 no output schema, the description provides a conceptual summary of the snapshot's contents but does not specify the return format (e.g., JSON object, list) or how to interpret values. For a parameterless tool with no output schema, this is minimally adequate but leaves room for ambiguity.

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 no parameters, and the schema description coverage is 100%. Per guidelines, baseline is 4 with 0 parameters since the schema already fully documents the input. The description adds no additional parameter meaning, which is acceptable.

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 'snapshot' of a 'second-order loop', listing specific components such as meta-learning velocity, RND curiosity, capability model, meta-consciousness score, and developmental stage. It distinguishes itself from sibling tools by combining these multiple meta-cognitive metrics into a single output.

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 obtaining a composite view of self-evidencing loop metrics but does not explicitly specify when to use this tool versus its sibling tools (e.g., tcai_meta_learning, tcai_curiosity). No when-to-use or when-not-to-use guidance is provided.

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