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slouchd

CyberChef API MCP Server

by slouchd

bake_recipe

Execute a sequence of CyberChef operations on input data to transform, encode, or decode it. Use a recipe of operations to derive the desired output.

Instructions

Bake (execute) a recipe (a list of operations) in order to derive an outcome from the input data

:param input_data: the data in which to perform the recipe operation(s) on
:param recipe: a pydantic model of operations to 'bake'/execute on the input data
:return:

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_dataYes
recipeYes
Behavior2/5

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

No annotations provided, so the description must carry full behavioral burden. It describes the action (bake/execute) but fails to disclose side effects, data mutability, or required permissions. Essential traits like destructiveness or state changes are absent.

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?

Description is short and front-loaded with the action, followed by param docs. No wasted words, though param docs could be more precise.

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?

Missing critical context: no output schema, no explanation of return values, ordering of operations, error handling, or relationship to sibling tools. For a tool with two required params and no annotations, this is insufficient for an AI agent to use correctly.

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?

The description adds basic param meaning: input_data is the data to operate on, recipe is the list of operations. However, it inaccurately calls recipe a 'pydantic model' when it's an array, and lacks detailed guidance on constructing the recipe. With 0% schema coverage, this is only modestly helpful.

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 the tool bakes/executes a recipe to derive an outcome from input data. It distinguishes from siblings like batch_bake_recipe who likely processes multiple recipes, but does not explicitly differentiate.

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?

No guidance on when to use this tool versus alternatives. The description implies usage for executing a single recipe, but lacks conditions, prerequisites, or exclusions.

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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