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doublegate

CyberChef MCP Server

by doublegate

cyberchef_magic

Read-onlyIdempotent

Analyze unknown input to detect encoding, compression, or file type, then suggest decoding steps. Unwrap nested encodings and filter results with a known plaintext regex to reveal meaningful data.

Instructions

The Magic operation attempts to detect various properties of the input data and suggests which operations could help to make more sense of it.OptionsDepth: If an operation appears to match the data, it will be run and the result will be ana...

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoLayers of encoding to unwrap. Default 3 handles the usual nesting (base64-then-gzip); raise only if output is still encoded. Cost grows fast.
inputYesThe input data to process
intensive_modeNoAlso brute-force XOR, bit rotations and encodings, not just detect them. Finds single-byte XOR; slow, and only the first 100 bytes are tried.
extensive_language_supportNoCompare against 284 languages instead of ~40. Usually widens the match list without sharpening it; the language result is an estimate either way.
crib_known_plaintext_string_or_regexNoRegex a decoding must match to be reported. The most effective filter when you know any of the plaintext -- a flag prefix, a header, an expected word.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv3.6.0

TDQS

B3.3/5.0
Behavior3/5

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

The description reveals that matching operations will actually be run and their results analyzed, which is useful behavioral context beyond the readOnly/idempotent annotations. The text is truncated at 'the result will be ana...', so the full behavior is not visible; this limits the transparency score.

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 opening sentence is informative, but the text is poorly formatted with run-together content like 'it.OptionsDepth:' and appears truncated. It is not excessively long, but the structure is messy enough to reduce clarity.

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 no output schema, the description should say more about what Magic returns—such as suggested recipes, confidence levels, or decoded output—but it only says it 'suggests which operations.' The parameter docs are strong, yet the missing output expectations and truncated description leave a moderate gap.

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 description coverage is 100%, and each parameter already has rich, behavior-oriented documentation such as cost growth, 100-byte limits, and language count trade-offs. The tool description adds little beyond what the schema already provides, so baseline 3 is appropriate.

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 states a specific purpose: detect properties of the input data and suggest operations to make sense of it. This clearly identifies the tool's function, though it does not explicitly differentiate it from sibling detection tools like cyberchef_entropy_scan or cyberchef_hash_identify.

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?

Usage context is implied rather than explicit: an agent can infer this is for unknown-format input that needs automatic analysis. However, there is no direct guidance about when to prefer Magic over alternatives like baking a known recipe or using a more targeted identifier.

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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