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

With no annotations, the description carries the full behavioral burden, and it does disclose a real access limitation (blackout unavailability with a legal citation) that an agent could not infer otherwise. It still omits confirmation that it is a read-only forecast tool and any indication of freshness or return format, so it only partly fills the annotation gap.

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?

Two sentences, front-loaded with what is returned and followed by the availability constraint. Every clause adds distinct information and nothing is redundant.

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 zero-parameter tool with no annotations and no output schema, the description covers both the returned data points and a key operational constraint (blackout window). It does not describe the shape of the response, but the enumerated metrics largely compensate.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies. The description usefully spends its text on output content instead of inventing parameter guidance.

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 and resource (Spanish general election 29 Nov 2026) and enumerates the exact outputs: seat forecast per list with median and 90% range, poll average, majority chances for three blocs, and strongest list. This clearly distinguishes it from siblings like get_spain_provinces_2026 (provincial breakdown) and get_latest_polls (raw polls).

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 gives an explicit when-not-to-use condition: the legal blackout 24–29 Nov 2026 under art. 69.7 LOREG, which is genuinely useful. However, it never states when to prefer this over siblings such as get_spain_provinces_2026 or get_election, so routing to alternatives is left to inference.

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