aetre_congestion_matching

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

With no annotations provided, the description carries the full burden, and it does disclose real behavioral content: the optimization enforces a hard per-reviewer utilization cap of rho <= 0.85 and prioritizes affinity. However, it does not state what happens when the constraint set is infeasible, whether the result is a recommendation or a state change, or what the tool returns.

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?

A single front-loaded sentence with zero filler: the main action leads, followed by the objective and the constraint. 'Kingman queue utilization constraints (rho <= 0.85)' is dense but precise, and every clause earns its place.

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 description nails the 'what' and 'why' but leaves output semantics unaddressed — there is no output schema and the description never hints at what the tool returns (e.g., an assignment plan, score matrix, or feasibility report). Given the algorithmic complexity and constraint-enforcement behavior, infeasibility handling is also a gap.

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, but the description adds value by tying parameters to the algorithm: domain/keyword affinity maps to proposal.domain/keywords and reviewer.domain/expertise_tags, and the rho <= 0.85 constraint operationalizes target_utilization. This semantic glue helps an agent understand why each parameter matters beyond its type and schema text.

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 opens with a precise verb-resource pair — 'Optimizes reviewer-to-proposal assignment' — and immediately specifies the dual objective: maximize domain/keyword affinity and enforce Kingman queue constraints (rho <= 0.85). This specificity makes the tool unmistakable among siblings like aetre_heterogeneous_queues and aetre_batch_triage.

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?

No explicit when-to-use or when-not-to-use guidance is given, and no alternative tools are named. The intended scenario (matching reviewers to proposals under workload caps) is only implied by the objective described, so an agent must infer applicability rather than being told.

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