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encode_toon

Convert JSON data into compact TOON format to reduce token usage in LLM prompts.

Instructions

Convert JSON data into compact TOON format to save tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jsonYesThe JSON data (as a string) to encode into TOON format.
indentNoNumber of spaces for indentation. Defaults to 2.
replacerNoArray of properties to include in the output. If not provided, all properties are included.
delimiterNoDelimiter for array values (comma, tab, or pipe). Defaults to comma.
keyFoldingNoCollapse single-key wrapper chains into dotted paths. Defaults to 'off'.
flattenDepthNoMaximum depth for key folding. Defaults to Infinity.
Behavior2/5

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It only states the conversion purpose without any details on side effects, idempotency, error handling, or permissions.

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 highly concise with a single front-loaded sentence. However, it could benefit from minimal structural elements to improve scannability.

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

Completeness2/5

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

The description lacks details on the return format (expected to be a string in TOON format) and does not address error conditions. Given the absence of output schema and 6 parameters, the description is incomplete.

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

Parameters3/5

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

Schema descriptions cover 100% of parameters. The description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.

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 verb (Convert), resource (JSON data into compact TOON format), and the benefit (save tokens). It effectively distinguishes from the sibling tool decode_toon.

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

Usage Guidelines3/5

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

The description implies usage when one wants to convert JSON to TOON format to save tokens, but lacks explicit guidance on when to use versus alternatives. No mention of prerequisites or when not to use.

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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