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christophejlegros-lgtm

ASTRA — Unified Research Lab + MCP Server

tcai_second_order

Snapshots a second-order self-evidencing loop, enabling the system to observe and correct its predictive capacity by tracking meta-learning velocity, curiosity, and meta-consciousness.

Instructions

Second-order (self-evidencing) loop snapshot: meta-learning velocity, RND curiosity (epistemic value), capability model, meta-consciousness score, developmental stage. The system observing and correcting its own predictive capacity (Legros 2026 §3.2).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the data returned (a snapshot) but does not explicitly state that the operation is read-only, nor does it disclose any side effects, authentication needs, or rate limits. The term 'snapshot' implies non-destructive reading, but this is not explicit.

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

Conciseness4/5

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

The description is concise in two sentences, front-loading the key components. However, the reference '(Legros 2026 §3.2)' is cryptic and may not be actionable for the agent.

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

Completeness4/5

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

Given the tool has zero parameters and no output schema, the description lists the main components of the snapshot, providing sufficient context for an agent to understand what will be returned. It is complete enough for a straightforward retrieval tool.

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

Parameters4/5

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

There are no parameters, and the schema coverage is trivially 100%. The baseline for zero parameters is 4, and the description adds no parameter information as none exist.

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

Purpose4/5

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

The description states it is a 'second-order loop snapshot' listing specific components (meta-learning velocity, RND curiosity, etc.), which clarifies its purpose as a composite retrieval tool. It distinguishes from siblings like tcai_meta_learning or tcai_curiosity by being a combined snapshot.

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?

No guidance on when to use this tool versus alternatives like individual tcai_* tools. Lacks any 'when to use' or 'when not to use' information.

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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