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format_json_visualizer

Read-onlyIdempotent

Parse JSON into a validated tree and structural summary with node counts, depth, and type statistics. Use to inspect shape of nested data.

Instructions

JSON Tree Visualizer and Structure Analyzer. Parse a JSON document into a validated tree and structural summary: confirms the JSON is well-formed, returns the parsed value, counts every node, and tallies key, depth and type statistics for exploring deeply nested data. Use format_json to pretty-print or minify JSON, data_json_path_evaluator to query nodes with JSONPath, data_json_schema_validator to check an instance against a schema, and webdev_json_to_typescript to emit TypeScript interfaces; use this tool when you only need to inspect shape, depth and node/type counts. Runs locally on the JSON you provide: read-only, non-destructive, contacts no external service, and is rate-limited (60 requests/minute for anonymous callers). Invalid JSON returns isValid:false with the parser error message instead of an HTTP error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jsonYesJSON document to parse and analyze, as a raw string. Blank input returns isValid:false with empty statistics; malformed input returns isValid:false with the parser error. The alias "input" is also accepted.
inputNoAlias for json (used when json is omitted).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
isValidNoTrue when the input parsed as well-formed JSON.
parsedNoThe parsed JSON value (object, array, or scalar); null when invalid or blank.
errorNoParser error message when invalid; empty string otherwise.
nodeCountNoTotal number of nodes (every value, including container nodes) in the parsed tree.
statisticsNoStructural tallies; null when input is invalid or blank.
Behavior5/5

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

Beyond annotations (readOnlyHint, destructiveHint, idempotentHint), the description adds valuable behavior: runs locally, no external service, rate-limited (60/min for anonymous), and behavior on invalid JSON (returns isValid:false with error message).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is informative but somewhat lengthy. It is well-structured with clear sections: purpose, details, usage alternatives, local execution, and error handling. Could be slightly more concise but efficient overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers all necessary aspects: what the tool does, its limitations, error behavior, rate limits, and differentiation from related tools. With an output schema present, the description is complete for this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with descriptions. The description adds edge-case behavior: 'Blank input returns isValid:false with empty statistics; malformed input returns isValid:false with the parser error. The alias "input" is also accepted.' This adds value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: parse JSON, validate, and provide structural summary (node counts, depth, type stats). It explicitly distinguishes from siblings like format_json, data_json_path_evaluator, etc., by specifying when to use this tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance: 'use this tool when you only need to inspect shape, depth and node/type counts' and lists alternatives for other tasks. It also clarifies that it runs locally and is read-only.

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

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