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

Goes well past the annotations by disclosing data provenance (verified promise data from the Parliamentary Intelligence Graph), a hard geographic coverage limit with the exact party list, and a candid AI-extraction/classification disclosure naming the models used, while noting original quotes and sources serve as evidence. That provenance and reliability framing is exactly what an agent needs to judge how much to trust the output.

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?

Front-loads purpose, then scope restriction, then the AI caveat — a sensible order with no filler sentences. The bracketed AI block is dense but earns its space as trust metadata; the em-dash party list is slightly heavy but not wasteful.

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 read-only, four-parameter tool with no output schema, the definition covers what it does, what data backs it, who it covers, and how results were produced. It leaves unstated the shape of the returned status categories and how `limit` interacts with result ordering, but nothing critical to a correct call 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 coverage is 100%, so the per-parameter meanings are already documented. The description still adds value the schema lacks: an explicit enumeration of valid party values (CDU, SPD, Grüne, FDP, Linke, AfD, BSW) and the name-collision warning about 'Grüne' (Austria vs. Germany), which prevents a common input error given no enum is defined.

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+resource: 'Prüft ob Parteien ihre Wahlversprechen gehalten haben', plus the underlying data product (Parliamentary Intelligence Graph). The scope restriction to German Bundestagsparteien further sharpens what the tool covers, but it never differentiates itself from the plausible sibling get_promise_journey, so it falls short of a 5.

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 — an agent can infer this is the tool for checking promise fulfilment. The firm exclusion ('keine österreichischen Parteien') is a data-scope constraint, not tool-selection guidance, and no alternative sibling is named for related needs such as tracing how a single promise evolved.

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