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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety and idempotency carry little weight here. The description adds real behavioral context beyond them: monthly cadence for debt, quarterly cadence for GDP ratios, deficit measured cumulatively from January with both plan and prior-year comparison, and a 12-month growth statistic. It does not disclose units/currency for amounts or how stale the 'latest month' figure can be, and the stray token 'finwire_public_debt_per_capita' in the middle of the sentence is opaque noise rather than disclosure.

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 whole definition is one long semicolon-chained sentence with no lead clause saying what the tool does, so the reader must parse to the end to understand the payload. It is packed with return-value detail that the output schema already covers, while a leaked internal identifier ('finwire_public_debt_per_capita') sits mid-sentence unexplained. Content is dense but not front-loaded and not structured.

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?

Since an output schema exists, the description's heavy enumeration of return fields is largely redundant, and what it omits matters more: currency/units for the debt amounts, whether 'latest month' lags the current date, and the meaning of the history parameter for the returned series. For a no-required-parameter read tool with full annotations this is adequate but leaves real questions open.

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% and there is a single optional parameter, so the schema already explains 'history' (0-120, default 12) fully. The description adds nothing about it — notably it never says the per-capita, growth or history series are affected by that parameter, so an agent cannot tell what 'history' actually extends. Baseline 3.

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 enumerates the resources precisely (State Treasury debt, general government debt % of GDP, cumulative state budget deficit) and even names the source (Ministry of Finance), so an agent can tell what data comes back. But it never states an action verb or frames the tool as a retrieval of Poland's public-debt dataset, and among 30+ sibling data-fetch tools there is no phrasing that distinguishes it from get_macro_indicator or get_macro_snapshot. Purpose is inferable rather than stated.

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?

There is no when-to-use guidance at all: no mention of when this belongs instead of get_macro_indicator, compare_macro_indicators, or get_europe_data, and no note that this is Poland-specific while get_europe_data covers other economies. The only implicit signal is the subject matter itself, which the agent must map to its task unaided.

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