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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), but the description adds real behavioral content: the modeling assumptions baked in (constant velocity, independent new errors, exact stations/clocks), the user is supplying future samples rather than measurements, and the output is explicitly not a measurement or visibility proof. That is meaningful context beyond the structured fields.

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?

Four tight sentences, front-loaded with the core action before the assumptions, prohibitions, and caveat. Dense jargon but no filler sentences. Slightly telegraphic phrasing is the only structural cost.

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?

The output schema exists, so return values need no explanation, and the description does cover assumptions and intellectual limitations. However, for a five-parameter, deeply nested tool with 20% schema coverage, it omits the constraint semantics (max_cost_units, min_elevation_deg), the horizon domain, and the observer-clock handling, leaving a real gap for correct invocation.

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 only 20%, so the description must compensate, and it partially does: it explains the candidate series semantics (future, not measured angles), the cost criterion, and the three assumption flags. Field-level meaning for objective.horizon_s, constraints, and the sigma parameters is still conveyed only by the schema's sparse descriptions, so it does not fully close 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?

States specific verbs and resources: it fits original tracks, then ranks 1–64 supplied future series by local-linear uncertainty and declared cost. That is far more than a restatement of the name. It does not, however, explicitly distinguish itself from nearby siblings such as assimilate_observation or stress_test_observation.

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 gives exclusions ('candidate angles and client baselines are forbidden') and a scope caveat ('prediction is not an acquired measurement or visibility proof'), which frames when the output is not sufficient. But it never says when to prefer this tool over assimilate_observation, stress_test_observation, or simulate_plan, so the positive selection criteria remain implied.

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