hyperspace_trigger_reconsolidation

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 usefully discloses the return shape ({ok, iterations, loss_before, loss_after}), which is real behavioral context given there is no output schema. But it never states cost/duration, whether the operation locks or mutates the collection irreversibly, or whether the collection must be frozen first — material gaps for a write-side operation.

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 sentences, front-loaded with the mode label followed by the action and return contract; nothing is padded. The 'AI Sleep Mode' prefix is jargon but functionally orients the agent before the details.

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 2-param mutating tool with no annotations and no output schema, describing the return object is a meaningful contribution. But the un-explained `learning_rate` and the absence of prerequisites, side effects, or cost left the definition short of what an agent needs to invoke it safely.

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%, so the description must explain both parameters; it only names `collection` implicitly and adds no meaning beyond the name. `learning_rate` (default 0.1) is never mentioned, so the agent has no idea what tuning it does or what sensible values are.

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?

Names a specific verb (trigger) and mechanism (Flow Matching optimization) scoped to a `collection`, and the word 'optimize the geometric representation' distinguishes it from read-only siblings like hyperspace_analyze_geometry. It stops short of naming the adjacent maintenance tools it could be confused with, but an agent can tell broadly what it does.

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?

'AI Sleep Mode' hints that this is a background/maintenance operation rather than a query, which is implicitly useful usage guidance. However, there is no explicit when-to-use or when-not-to-use versus hyperspace_analyze_geometry, hyperspace_freeze_collection, or hyperspace_rebuild_index, all of which touch the same collection state.

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