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

Annotations already declare readOnly, idempotent, closed-world and non-destructive, so the safety profile is covered. The description adds data-handling behavior that annotations cannot: source dashes stay missing rather than becoming zeros, and Russia/Ukraine are folded into SU while US/Canada sit in NA_OC. It does not describe the return payload (counts vs. shares) or how row counts scale with the age/country filters.

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?

It is short (three clauses) and front-loads the source table, but the leading position is given to 'CBS table2.8' rather than the task, and the missing spaces and bare abbreviations make it harder to parse than a plain sentence. Nothing is strictly redundant, though the ordering could better serve an agent deciding whether to call it.

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?

No output schema exists, so the description carries the burden of describing return values, and it never says whether the result is a population count, a share, or a row set keyed by age band and country. For a 4-parameter, filter-heavy read tool with rich annotations, the remaining gap is the response shape rather than the domain boundary.

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 the schema already documents every parameter, so this is mostly reinforcement. However, the description adds genuine cross-parameter meaning absent from the schema prose: the country grouping rules (SU, NA_OC) and the fact that dashes are missing values rather than zeros, which directly determines how an agent should interpret the region and country inputs. It repeats heavily, so without that added grouping semantics it would be a 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 names a concrete resource: annual-average 2023 national population of Jews and Others born abroad, disaggregated by 25 published countries/groups and 8 age bands from CBS table 2.8. It also draws boundaries against adjacent datasets ('Not Census2022, arrivals or city data'), which lets an agent separate it from demographics/immigration/locality tools. The verb is only implied and the run-together tokens ('table2.8', 'annual-average2023', 'by25') slightly muddy the statement.

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 real when-not guidance by excluding Census 2022, immigration-arrival, and city-level questions, which is the main risk of misrouting among these siblings. It stops short of naming the specific tool to use instead (e.g. get_demographics, get_immigration, get_locality), and there is no explicit positive 'use this when...' statement.

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