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

With no annotations, the description carries the full burden and does disclose meaningful behavior: the d'Hondt method, the multi-level bottom-up aggregation, and the restriction to normalized strategies. It omits, however, the return structure, potential error behavior, or computational cost for a heavy simulation. The methodological disclosure is real but incomplete for a complex tool.

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 dense sentence that front-loads the core action (seat division) before the method and constraint. It is efficient and wastes little, though the parenthetical model chain adds weight.

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 5-parameter, complex simulation tool with no output schema and no annotations, the description explains the computation but says nothing about the returned data structure or how period/survey selectors affect results. The well-documented schema covers inputs, but the agent lacks visibility into output shape.

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 all five parameters including period formats and the enum for strategy. The description adds a reinforcing constraint about normalized-only strategies but no syntax or format detail beyond the schema. Baseline 3 applies when the schema does the heavy lifting.

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 states a specific verb and resource — simulating the division of 460 Sejm seats via the d'Hondt method — and specifies the data basis (averaged polls from a chosen period) and model (bottom-up 380 counties -> 41 districts -> national). This clearly distinguishes it from siblings like get_national_projection or get_district_projections, which produce vote projections rather than seat allocations.

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?

The description implies the context (seat allocation from aggregated polls) and discloses a key constraint — only normalized strategies are allowed. However, it never states when to prefer this tool over get_national_projection or the other projection siblings, nor any prerequisites. Usage is inferable but not explicit.

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