SRG SSR Polis – Schweizer Wahlen

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds meaningful context by stating the historical depth (back to 1900), the available filters, and the returned fields. No contradiction with annotations.

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 description is compact and well structured: one action sentence, followed by use case, important notes, and a concrete example. Every element earns its place and there is no redundant wording.

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 simple filtered list tool with an output schema and strong annotations, the description provides enough to call it correctly: source system, data domain, historical range, filters, and sample usage. Pagination defaults are visible in the schema, so nothing critical is missing.

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 0%, so the description must compensate. It adds meaning for year/canton filters and gives an example (year_from=2023), but it does not explain page, page_size, or year_to semantics. Pagination is left entirely to the schema names and defaults.

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 uses a specific verb and resource: it retrieves Swiss National Council, Council of States, and cantonal elections from the Polis system and lists the returned fields (date, designation, ID). The domain 'Wahlen' clearly separates it from the sibling 'Votations' tools without needing to open the schema.

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 names concrete use cases (historical election analyses, journalistic research) and states capabilities (filter by year/canton, data since 1900). It does not explicitly mention when not to use it or point to sibling alternatives such as get_votations, so it is clear but not fully prescriptive.

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