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slouchd

CyberChef API MCP Server

by slouchd

perform_magic_operation

Automatically detect encoding and decode data using recursive pattern matching and speculative execution, with optional intensive mode and extensive language support.

Instructions

CyberChef's magic operation is designed to automatically detect how your data is encoded and which operations can be
used to decode it

:param input_data: the data in which to perform the magic operation on
:param depth: how many levels of recursion to attempt pattern matching and speculative execution on the input data
:param intensive_mode: optional argument which will run additional operations and take considerably longer to run
:param extensive_language_support: if this is true all 245 languages are supported opposed to the top 38 by default
:param crib_str: argument for any known plaintext string or regex
:return:

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_dataYes
depthNo
intensive_modeNo
extensive_language_supportNo
crib_strNo
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions 'speculative execution' and that intensive_mode takes longer, providing some behavioral insight. However, it lacks details on side effects, authentication needs, or limits.

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

Conciseness3/5

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

The description is a docstring with parameter explanations, which is structured but somewhat verbose. It could be more concise by separating the core function from parameter details.

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

Completeness3/5

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

With 5 parameters, no output schema, and complex behavior, the description covers parameter purposes but does not explain return values or constraints. It is adequate but not fully complete.

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?

Schema description coverage is 0%, but the description explains each parameter: input_data, depth, intensive_mode, extensive_language_support, and crib_str. This adds significant meaning beyond the schema's raw fields.

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

Purpose4/5

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

The description clearly states that the tool automatically detects encoding and operations to decode data, which is a specific verb-resource purpose. It distinguishes from sibling tools like bake_recipe and batch_bake_recipe by focusing on automatic detection rather than direct baking.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. With sibling tools present, there is no explicit mention of trade-offs or exclusion criteria.

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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