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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the core behavior (state advancement, returned metrics, optional positions) and adds a useful seed-state fact (fresh flocks start with polarization near 0.05). However, it leaves the mutating and irreversible nature of stepping implicit — it never states that the flock cannot be rewound or what happens once a simulation reaches an end state.

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 operational sentence is tight, front-loaded, and zero-waste. The open-question paragraph is roughly a third of the text and is not strictly needed for invocation, though it does provide behavioral context (ordering dynamics) and a plausible intended use. Slightly more than minimal, but every part carries some information.

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?

With no output schema and no annotations, the description compensates well by naming all three returned metrics and the optional per-bird positions, and it adds a seed-state behavioral fact. All parameters are schema-documented. Remaining gaps are minor: irreversibility of stepping and end-of-simulation behavior are not addressed, but the essentials for a correct call are present.

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 schema already documents steps, flock_id, and include_positions; the baseline is 3. The description adds value by naming the output metrics produced by stepping, which enriches the agent's understanding of what steps/flock_id accomplish, but it provides no new per-parameter detail beyond the schema. Baseline 3 is appropriate.

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 first sentence states a specific verb ('Advance'), a resource ('a flock'), a quantified scope (1-1000 ticks), and the exact metrics returned (polarization, cluster_count, mean_neighbor_distance). This clearly differentiates it from siblings like flock_create (creation), flock_get (reading state), and flock_set_params (configuration). The 'open question' aside adds context but does not obscure the operational purpose.

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: to observe a flock at a later simulation time, call flock_step. The open question hints at a concrete scenario (repeatedly stepping until polarization exceeds 0.5), but the description never explicitly states when to choose this over alternatives like flock_get or flock_timeline, and gives no exclusions. This is clear context without explicit routing, so it sits at implied usage rather than full guidance.

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