set_native_team_attitude

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

Annotations already declare this is a non-readonly, idempotent, destructive write, but the description adds real behavioral context beyond them: the solver is installed once per process (a global side effect), the table is symmetric, unset pairs fall back to engine defaults (same team -> Friendly, else Hostile), and the setting feeds AI Perception. It does not state what existing attitudes are overwritten or any auth/permission needs.

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 first sentence front-loads the action, the fallback rule follows immediately, and the return shape is listed separately. It is dense with jargon but every clause earns its place given the complex, global nature of the operation.

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 mutation tool with annotations already covering safety, the description adds the installation scope, the fallback semantics, the downstream AI Perception dependency, and the return fields (no output schema exists). An agent has everything needed to call and interpret it correctly.

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 coverage is 100%, so the baseline is 3. The description goes further by clarifying the pairwise/symmetric relationship of team_a and team_b and by distinguishing the requested 'attitude' from the engine-returned 'resolved_attitude', which the schema does not convey.

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: it installs a custom AttitudeSolver, records a pairwise attitude for team_a/team_b, and drives engine AI Perception affiliation filtering. The mechanism is unambiguous. However, it never names the sibling it differs from (e.g., set_actor_team / get_actor_team), so the differentiation is left to inference.

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?

Usage is implied rather than stated: the description explains that this drives Sight/Hearing/Touch DetectionByAffiliation and that install happens once per process, so an agent can infer when it matters. But there is no explicit 'use this when X, not Y' guidance, no prerequisites, and no named alternative such as set_actor_team.

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