json_read

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 describes the tool's exploratory nature and high-level analysis purpose, but doesn't mention performance characteristics, error handling, memory usage with large files, or output format details. It adds some context about the tool's role but lacks comprehensive behavioral traits.

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 perfectly concise with two well-structured sentences. The first sentence states the core purpose, and the second provides clear usage guidelines. Every word earns its place, and the information is front-loaded with no redundancy or unnecessary elaboration.

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 read-only JSON analysis tool with no annotations and no output schema, the description provides good context about when and why to use it. However, it doesn't describe what the output looks like (structure, format, or content), which is a gap given the lack of output schema. The purpose and usage guidance are complete, but output expectations are missing.

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 8 parameters thoroughly. The description doesn't add any specific parameter semantics beyond what's in the schema - it mentions general concepts like 'explore JSON structure' and 'understand data schema' but doesn't explain how individual parameters contribute to these goals. Baseline 3 is appropriate when schema does the heavy lifting.

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 ('read and analyze JSON') and distinguishes it from its sibling by specifying it's for exploration and understanding structure rather than extraction. It explicitly mentions 'use this for initial data exploration' and 'before extracting specific values', which differentiates it from json_extract.

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 ('for initial data exploration', 'to understand shape and types of data before extracting specific values') and when to use alternatives ('use this... before extracting specific values', implying json_extract is for extraction). It clearly defines the tool's role in the workflow.

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