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encoding_decoding_bubble_babble

Read-onlyIdempotent

Encode binary data into a pronounceable Bubble Babble string with checksum, or decode it back to original bytes. Useful for human-readable representations like SSH key fingerprints.

Instructions

Bubble Babble Encoder/Decoder. Bubble Babble encodes arbitrary binary data as a pronounceable string of alternating consonants and vowels (e.g. "xexax", the format SSH uses for key fingerprints), with a built-in checksum. Set operation to encode to turn input into babble, or decode to recover the original bytes. Use encoding_decoding_base64 instead when you need compact, standard ASCII-safe transport rather than a human-pronounceable, memorisable form. Runs locally on the input you provide: read-only, non-destructive, offline, and rate-limited. Returns the converted result plus a length/byte analysis of the input.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesData to process. When encoding, interpreted per format; when decoding, the Bubble Babble string (e.g. xexax).
operationYesencode turns input bytes into Bubble Babble; decode recovers the original bytes.
formatNoHow text is read on encode and rendered on decode. text is raw UTF-8, hex is hexadecimal, binary is space-separated 8-bit groups.text

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoWhether the operation succeeded.
inputNoThe input text, echoed back.
operationNoThe operation performed (encode or decode).
formatNoThe format applied (text, hex, or binary).
resultNoThe Bubble Babble string (encode) or recovered data in the chosen format (decode).
infoNoOne-line explanation of Bubble Babble encoding.
analysisNoMetrics about the input.
Behavior4/5

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

Description adds details about built-in checksum, offline operation, and length/byte analysis, complementing annotations (readOnlyHint, idempotentHint) without contradiction.

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?

Concise single paragraph with front-loaded purpose and useful detail; slightly redundant in repeating 'Bubble Babble' but overall efficient.

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?

Completely covers purpose, usage, alternatives, behavioral traits, and return value (converted result plus analysis); output schema further reduces need for return details.

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?

Description explains the effect of 'operation' (encode/decode) and 'format' (text/hex/binary) beyond the schema, including how inputs are interpreted, adding value despite high schema coverage.

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?

Clearly states the tool is an encoder/decoder for Bubble Babble, gives example usage ('xexax'), and explicitly contrasts with sibling tool encoding_decoding_base64, differentiating when to use each.

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

Usage Guidelines4/5

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

Provides explicit alternative (base64) and mentions local, read-only, non-destructive, offline, and rate-limited behavior, but lacks more detailed when-not-to-use guidance beyond the base64 comparison.

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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