hyperspace_predict_momentum

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

With no annotations, the description must carry the full behavioral burden. It discloses that this is a forecast operation returning a JSON trajectory summary, but says nothing about whether it is read-only, how expensive it is across many trajectory_ids, or whether results are deterministic for a given horizon.

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?

Three tight sentences that lead with the purpose, then requirements, then output. Nothing is wasted, though the terminology could be simpler.

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?

There is no output schema, and 'predicted trajectory summary as JSON' is too vague to tell an agent what fields come back or how to read them. The role of `curvature` is also never explained, so the definition is only adequate.

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 100%, so the baseline is 3. The description's 'steps sets prediction horizon' simply restates the schema's 'Steps to predict ahead', and the unexplained `curvature` parameter is left entirely to the schema.

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?

States a specific verb and resource: forecasting future agent thought paths via Koopman momentum extrapolation. That is clearly distinct from siblings like hyperspace_analyze_thought_stability or hyperspace_graph_traverse. It stops short of 5 because the jargon ('Koopman momentum extrapolation') is never grounded in plainer terms.

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?

It says what is required (`collection` and `trajectory_ids`) and what `steps` controls, which implies the forecasting scenario, but gives no when-to-use versus alternatives such as analyze_thought_stability and no preconditions or exclusions.

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