tcai_self_model

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

Annotations already declare this a read-only, idempotent, non-destructive, closed-world read, so safety is covered. The description adds modest value by enumerating what the state comprises (which is effectively a hint at the return contents given there is no output schema), but it says nothing about freshness, cost, or how the facets are scoped.

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?

It is a single compact, front-loaded phrase with no filler. The trade-off is that it is perhaps too clipped — one clarifying clause about the return shape would have earned more, but nothing is wasted.

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?

For a zero-parameter, no-output-schema state reader, an agent would want to know roughly what comes back and how it differs from the other tcai state tools. The description gives the conceptual components but no shape or relationship to siblings, leaving it adequate but incomplete.

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 takes zero parameters, so the schema has nothing to document and the baseline is 4. There are no parameter semantics for the description to clarify.

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 names the resource (self-model / self-representation state) and enumerates its facets — interoception, epistemic model, temporal continuity, attention schema — which distinguishes it somewhat from generic state readers. However, it is a bare noun phrase with no verb, so it never states that it *retrieves* or *reports* this state; the read behavior must be inferred from readOnlyHint. It also doesn't differentiate from nearby siblings like tcai_workspace_state or tcai_metaconsciousness.

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?

There is no guidance on when to call this tool, no prerequisites, and no mention of alternatives. With siblings such as tcai_metaconsciousness, tcai_second_order, and tcai_workspace_state all plausibly overlapping with 'self-representation', the absence of routing guidance is a real gap.

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