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

With no annotations, the description carries the full burden. It does disclose a meaningful behavioral trait: that only subspaces the caller may see are included, plus the recursive depth ('any depth'). However, it omits that this is a read operation, auth requirements, and any pagination or size limits for large trees.

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?

Two tight sentences, front-loaded with the resource. The second sentence enumerates return fields compactly. Slightly dense but no wasted filler.

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 output schema, the description usefully enumerates the returned node fields, which is the right compensation. But for a tool with no annotations it omits input-parameter meaning and usage context, leaving gaps an agent must guess at.

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?

Schema description coverage is 0% and there is one required parameter (slug) that the description never mentions. The phrase 'A space and every subspace below it' implies a target space but provides no format, source, or semantics for the slug. It fails to compensate for the coverage gap.

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 conveys that this returns a space plus all descendant subspaces as a nested structure, including the output fields per node. That is specific enough for an agent to understand the resource and shape of the result. It stops short of naming siblings (e.g. get_space, list_spaces, read_space) to disambiguate which of these to choose.

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 when-to-use guidance and no comparison against alternatives. Given siblings like brainkb_get_space, brainkb_list_spaces, and brainkb_read_space, the agent is left to infer when a full recursive tree is preferable to a flat space lookup. No prerequisites or exclusions are stated.

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