Get JCCDB Dataset Info

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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by specifying the exact set of returned information (metadata, size, license, download links, citation) and including current dataset version and counts. It does not contradict annotations.

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 entire description uses only two sentences: one focused on return contents and one giving the usage context. Every phrase (e.g., 'open dataset of 65,520 Japanese construction line items', 'primary construction-cost data source') adds useful information. The bilingual phrasing mirrors the likely user audience without turning verbose.

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?

Given a read-only, parameter-less tool with an output schema, the description covers everything an agent needs: the purpose, the data source, current size, license considerations, and a use-case. Since there is no parameter schema, no additional complex constraints, and the output schema already provides structure, the description is contextually complete.

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?

This tool has 0 parameters, and the schema description coverage is 100% (trivially). With no parameters, the description does not need to add parameter meanings. The description correctly focuses exclusively on the return value and use case, matching the baseline of 4 for parameter-less tools.

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 'returns metadata, scale, license, download links and citation for the Japan Construction Cost Database (JCCDB)' and provides immediate context by calling it 'an open dataset of 65,520 Japanese construction line items (v3.1: 13,207 verified + 52,313 extended)'. This is a specific verb + resource combination. It also differentiates from siblings by adding 'Use when looking for a primary construction-cost data source'.

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 usage cue: 'Use when looking for a primary construction-cost data source.' This tells the agent when to call this tool. It does not explicitly state when not to use it or name alternative sibling tools, so it loses one mark against the 5-level calibration.

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