json_dry_run

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it returns size information in bytes, mirrors the shape structure in output, and handles arrays by returning total size of all matching elements. However, it lacks details on error handling, performance implications, or rate limits, which could be relevant for a tool processing potentially large JSON files.

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 front-loaded with the core purpose, followed by detailed parameter explanations. It uses bullet points and examples efficiently, but could be slightly more concise by integrating the 'Note' section into the main text or reducing redundancy in examples.

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 complexity (analyzing JSON size with shape objects) and lack of output schema, the description does a good job of explaining what the tool returns. It covers input semantics and behavioral traits adequately. However, without annotations or output schema, it could benefit from more detail on error cases or output format specifics to be fully 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?

The schema description coverage is 100%, so the baseline is 3. The description adds significant value by explaining the 'shape' parameter with detailed examples and notes on how it affects output (e.g., using 'true' for total size, nested objects for breakdowns, and array handling). This clarifies semantics beyond the schema's technical definition, though it doesn't add much for 'filePath' beyond what the schema already states.

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: 'Analyze the size breakdown of JSON data using a shape object to determine granularity.' It specifies the verb ('analyze'), resource ('JSON data'), and method ('using a shape object'), distinguishing it from sibling tools like json_filter and json_schema by focusing on size analysis rather than filtering or schema extraction.

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: 'Use json_schema tool to understand the JSON structure first.' This indicates a prerequisite step, helping the agent sequence operations correctly and avoid misuse by analyzing data without prior structural understanding.

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