brain_status

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful context by listing the returned diagnostics, but it doesn't disclose additional behavioral traits like authentication needs, freshness guarantees, or potential failure modes.

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 action-first sentence with no filler. Every clause adds distinct value: the operation, the resource, and the specific output categories.

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 diagnostic tool with thorough safety annotations, the description is complete. It covers the important return categories even without an output schema, and nothing critical appears missing for an agent to decide to call it.

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, so the description doesn't need to explain parameter semantics. The baseline of 4 applies because there is no parameter burden to address.

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 states a specific verb ('Get'), a clear resource ('system diagnostics and memory statistics for Local Brain'), and enumerates the exact return categories. This clearly distinguishes it from sibling memory-operation tools like brain_recall, brain_learn, brain_forget, and brain_prune.

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 clearly implies use when the agent needs system diagnostics or memory statistics rather than memory operations. It doesn't explicitly name alternatives or exclusion conditions, but for a zero-parameter status tool the usage context is clear enough.

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