japan_industry_benchmarks

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

With no annotations provided, the description carries the full burden and discloses important behavior: 'Runs fully offline', 'returns the benchmark row(s) ... with the available metrics and their data year', and 'this Free edition includes a subset of industries'. It also states the data source and the searched-as-is behavior for English names. It doesn't cover failure modes, but the core behavior is well disclosed.

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 dense but not wasteful; each clause contributes metrics, offline behavior, scope limitation, workflow, or input guidance. The main verb and resource are front-loaded, and the later sentences add rather than repeat. It is one long paragraph rather than tight bullets, but it remains efficient.

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?

For a 2-parameter read tool with no output schema and no annotations, the description covers the source, the exact metrics, the return content, the edition limitation, the intended companion tool, and parameter conventions. It doesn't describe the precise response shape, but the 'benchmark row(s)' statement gives enough context for invoking the 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 coverage is 100%, so the baseline is 3, but the description adds value by mapping metric codes to Japanese labels ('operating margin (売上高営業利益率)') and by providing preferred Japanese industry examples beyond those in the schema. This helps an agent supply correct parameter values, especially for metric and industry naming.

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 opens with a specific verb and resource: 'Retrieve sector-level financial benchmarks for Japanese industries'. It names the exact survey source and enumerates the available metrics, making the tool's purpose unmistakable. It also distinguishes itself from company-specific siblings by explicitly framing it for sector benchmarking.

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 an explicit use case: 'benchmark a company's profitability against its sector' and tells the agent to pair it with edinet_financials_usgaap for company-side figures. It lacks explicit 'when not to use' exclusions, but the context is clear enough to route an agent correctly.

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