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

With no annotations provided, the description carries full responsibility for behavior disclosure. It meticulously explains that trends are computed, that free callers receive only the last 3 years of series, that `locked` fields indicate Pro-only access, and cautions against misinterpreting short windows. It also discloses grain-specific behavior (trendMetrics only for postal areas), which is essential context.

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 long but well-structured, front-loading the core purpose and then layering details about defaults, trendMetrics, and free-tier limitations. Every sentence adds relevant information, though a few could be tightened without losing meaning.

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?

The description covers many critical aspects: data returned, computed changes, defaults, grain differences, free-tier limitations, and interpretation guidance. However, without an output schema, it could be more explicit about the exact response structure (e.g., field names for the time series and how `locked` is represented). This prevents a perfect score.

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 baseline is 3. The description adds valuable meaning beyond the schema: it states that variables default to housing prices and population, and explains that selecting a postal area yields `trendMetrics` (which the schema does not mention). This clarifies parameter choices and their downstream effects.

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 clearly states 'Time series for one Finnish postal area or municipality, with the direction and size of change computed for each metric' — a specific verb, resource, and scope. It distinguishes itself from siblings by focusing on single-place historical trends rather than comparisons or rankings, and includes concrete examples of the questions it answers.

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 provides clear context for when to use this tool via example questions like 'have apartment prices been rising in Espoo?' and 'is the population growing?'. It does not explicitly name alternatives or exclusions, but the intent is unambiguous, and it mentions `list_variables` in the schema for related functionality.

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