json_dry_run

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

With no annotations provided, the description carries full burden and does well at disclosing behavioral traits: it explains what the tool returns ('size information in bytes', 'mirroring the shape structure'), clarifies array handling ('returns total size of all matching elements'), and mentions the need to understand JSON structure first. It doesn't cover potential limitations like file size constraints or error conditions, but provides substantial operational context.

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 appropriately sized and front-loaded: the first sentence clearly states the tool's purpose and output. The subsequent examples and notes are necessary for understanding the shape parameter's complex usage. While somewhat lengthy due to the examples, every sentence serves a clear purpose in explaining this analytical tool's behavior.

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 the tool's analytical complexity and lack of output schema, the description provides substantial context: it explains the return format ('mirroring the shape structure but with size values'), array handling, and relationship to sibling tools. The main gap is the absence of output schema documentation, but the description compensates well with clear behavioral explanations for this size analysis 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 the schema already fully documents both parameters. The description adds some value by explaining the shape parameter's purpose ('defining what to analyze for size') and providing extensive examples, but doesn't add semantic meaning beyond what the schema's examples and descriptions already provide. This meets the baseline 3 for high schema coverage.

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's purpose with specific verbs ('analyze', 'determine', 'returns') and resources ('JSON data', 'size breakdown', 'bytes for each specified field'). It distinguishes from sibling tools by focusing on size analysis rather than filtering or schema extraction, with explicit mention of using json_schema first for structure understanding.

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 provides explicit guidance on when to use this tool vs alternatives: it states 'Use json_schema tool to understand the JSON structure first', clearly positioning this as a follow-up analysis tool. The shape parameter examples illustrate specific use cases, and the tool's focus on size analysis differentiates it from json_filter (for filtering) and json_schema (for structure).

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