Health Statistics

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered elsewhere. The description's only added value is disclosing what the metadata contains, which is useful but modest; it does not say whether data points are included versus the separate get_series tool.

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?

One front-loaded sentence: the verb and resource come first, followed by a dense, comma-separated list of contents with no filler or redundancy.

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?

With no output schema, the description does the job of enumerating return contents, and annotations cover safety and idempotency. It lacks any statement of when this is preferable to inspect_dataset or get_series, so it is not fully complete for selecting among the siblings.

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?

There is a single parameter with 100% schema description coverage, and that schema text ('Dataset id from search_catalog') actually carries the more useful context. The description adds nothing about the id parameter, so the baseline 3 applies.

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 (get full metadata for one dataset) and enumerates the returned fields (description, source, period range, dimension labels, default view, covered countries). It does not explicitly differentiate from the sibling inspect_dataset, which plausibly overlaps in scope, 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?

The description gives no when-to-use guidance and names no alternatives, even though inspect_dataset appears to be a close sibling. The only routing hint ('Dataset id from search_catalog') lives in the schema, not the description.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful behavior beyond that: chronological ordering, default-country fallback, and the default point cap with 'most recent kept' retention policy. It stops short of return-format or truncation signalling, but adds real value.

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 tight sentences; the core purpose and ordering constraint are front-loaded, and the optional-selection guidance follows without padding.

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 return semantics; it discloses ordering and the max_points retention rule, which is the key consumer-facing detail. A note on what happens when no exact series matches would make it fully self-contained.

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 baseline is 3. The description goes beyond the schema by giving concrete period syntax examples (2015, Q1/2015, 01/2015) where the schema only shows '2015', and by explaining that dimension values come from inspect_dataset.

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 ('Time series values for one country from a dataset') plus scope (single country) and ordering (oldest first). This is clearly distinguishable from the sibling tools get_dataset, inspect_dataset, and search_catalog.

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 routes the agent to inspect_dataset for obtaining dimension values and explains the optional series-selection path and start-period usage. It doesn't explicitly state when to prefer this over get_dataset, but the workflow context is clear.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety is covered structurally. The description adds the non-obvious behavioral trait that this returns only a preview (dimensions + "latest few values") rather than the full data, which is real context beyond the annotations. It doesn't mention rate limits or sampling window specifics, so not a 5.

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 with no waste: sentence one lists outputs, sentence two gives placement and purpose. Front-loaded and appropriately sized for a two-parameter read tool.

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?

With no output schema, the description carries the burden of describing returns and does so reasonably (dimensions, default series, latest few values). It is slightly incomplete on how many values constitute "latest few" and on behavior when country is omitted, but it is adequate for a read-only preview tool.

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 description coverage is 100%, so both id and country are already documented, giving a baseline of 3. The phrase "for a country" reinforces that country scopes the sample, but adds no syntax or default behavior beyond the schema's "Optional country, e.g. 'France'."

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 ("Show") and resource (one dataset) and enumerates what is surfaced: dimensions, default series, and latest values. It implicitly separates itself from get_series by framing it as a fit-check before "pulling the full series," but never names get_dataset or get_series directly, so sibling differentiation is partial.

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?

Gives explicit sequencing guidance ("Use after search_catalog") and the purpose condition ("check the dataset fits before pulling the full series"), which routes the agent well. It stops short of naming when-not-to-use or the explicit alternative tool for the full pull.

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

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds real value beyond them: it discloses the return shape ('dataset cards, not values') and the soft ranking semantics of the country filter ('prefer datasets covering it'), which the agent cannot infer from the hints alone.

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?

Four short, front-loaded sentences with zero filler. Purpose, workflow position, filter scope, and return shape each earn their place.

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?

With no output schema, the description carries the burden of describing returns and does so adequately by contrasting cards against values. It does not say what fields a card contains or how many are returned by default beyond the schema's limit description, leaving a small gap for a discovery tool an agent must chain from.

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 description coverage is 100%, so every parameter (limit, query, country, source_name) is already documented in the schema, including the country 'prefer' nuance. The description only restates the optional filters at a high level and adds no format or syntax detail beyond the schema, so baseline 3 applies.

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 (find) and resource (official health datasets) and enumerates the actual sources covered. It also positions itself in the family with 'Use first to get dataset ids' and distinguishes itself from get_dataset/get_series by declaring it returns cards, not values.

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?

Gives explicit sequencing guidance ('Use first to get dataset ids') and a clear when-not-to-use signal ('Returns dataset cards, not values'), which routes the agent to get_series/inspect_dataset for actual values. It stops short of naming those siblings directly, so the routing is inferable rather than spelled out.

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