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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: no auth required, Public Data License v1.0 with attribution required and commercial reuse permitted, and the data source (dashboard.e-stat.go.jp). It also clarifies that omitting region_code returns all regions the indicator has data for, which is a useful behavioral detail beyond the schema.

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 well-structured and information-dense. It front-loads the core purpose, then provides usage prerequisites, a concrete example, and licensing/source information. While it's longer than some descriptions, every sentence adds value. The only minor issue is that the licensing and data source details could arguably be in annotations, but they are relevant context for an agent deciding whether to use this data.

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 is complete for an agent to call this tool correctly. It covers what the tool does, prerequisites (calling indicator_info and region_info), parameter notation, a concrete example, data source, auth requirements, and licensing. The output schema exists, so return values don't need to be described. The sibling tools for reference lookups are clearly distinguished.

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 the schema already documents all parameters well. The description adds value by explaining the time notation format ('2020CY00' for calendar year, '20210100' for monthly), clarifying the region code hierarchy (00000 = Japan, 5-digit prefecture codes, 3-digit ISO country codes), and noting that indicator_code can be comma-separated for multiple series. This goes beyond the schema's basic descriptions.

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 the tool fetches time-series values for e-Stat Dashboard indicator codes, with optional region and time filtering. It names the specific domains (population, labour, economy, trade) and provides a concrete example. It distinguishes itself from sibling tools like estat-japan-dashboard.reference.indicator_info and estat-japan-dashboard.reference.region_info by explicitly instructing the agent to call those first.

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 explicitly tells the agent when to use this tool and what to do before calling it: 'Always call estat-japan-dashboard.indicator_info first to confirm units and valid cycle for the indicator, and estat-japan-dashboard.region_info to confirm region codes.' This is clear usage guidance that prevents incorrect calls. It also provides a concrete example with specific parameter values.

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