tcai_workspace_state

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only lists state labels and does not indicate whether this is a read-only operation, what the response format is, whether it triggers side effects, or any permissions required. The lack of behavioral context makes the tool unpredictable for an AI agent.

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 a single, efficient line with no filler or unnecessary words. It is front-loaded with the main topic and then enumerates specific state aspects. The lack of a verb is a content issue, not a conciseness issue, so conciseness merits a full score.

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?

With no annotations and no output schema, the description must stand alone, but it is too sparse. It names several state dimensions but does not explain their meaning, format, or interpretation. The tool appears to be a state accessor, but the description does not provide enough context for correct invocation or expectation of results.

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 accepts zero parameters, so there is no obligation to document parameter meaning. The schema is empty, and the baseline for zero-parameter tools is 4. The description's list of state components likely refers to output fields, not inputs, so no additional parameter semantics are needed.

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 is a noun phrase listing components ('ignition, focus, qualia, sync R, unity metrics, access history') with no verb indicating the tool's action. It doesn't explicitly state whether the tool retrieves, computes, or updates state, making the purpose ambiguous. It does list specific metric names, slightly distinguishing it from siblings, but the core action is missing.

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 provides no guidance on when to use this tool versus alternatives like tcai_metrics, get_metrics, or tcai_self_model. There are no context cues, prerequisites, or exclusions mentioned, leaving the selection entirely to inference.

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