About CyberChef API MCP Server
CyberChef API MCP Server is an MCP server that wraps the CyberChef Server API, letting MCP clients and LLMs use CyberChef operations, recipes and the magic operation. It suits users who want AI assistance with encoding, decoding and data transformation.
Key features
- 01Browse CyberChef operations by category
- 02Bake single or batch recipes
- 03Magic operation to detect data encoding
- 04Configure CyberChef API URL via environment variable
Use cases
- 01Letting an LLM decode unknown or obfuscated data
- 02Applying one recipe to a batch of inputs
- 03Exploring available CyberChef operations from an MCP client
MCP support
This is an MCP server that exposes two resources (operation categories and operations per category) and three tools (bake_recipe, batch_bake_recipe, perform_magic_operation) to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMECyberChef API MCP Server
This model context protocol (MCP) server interfaces with the CyberChef Server API. Allowing you to use any LLM/MCP client of your choosing to utilise the tools and resources within CyberChef.
🧰 Available Tools and Resources
get_cyberchef_operations_categories: resource - gets updated Cyber Chef categories for additional context / selection of the correct operationsget_cyberchef_operation_by_category: resource - gets list of Cyber Chef operations for a selected categorybake_recipe: tool - bake (execute) a recipe (a list of operations) in order to derive an outcome from the input databatch_bake_recipe: tool - bake (execute) a recipe (a list of operations) in order to derive an outcome from a batch of input dataperform_magic_operation: tool - perform CyberChef's magic operation which is designed to automatically detect how your data is encoded and which operations can be used to decode it
📝 Usage
Start the server using the default stdio transport and specifying an environment variable pointing to a CyberChef API
CYBERCHEF_API_URL="your-cyberchef-api-url" uv run cyberchef_api_mcp_server
🧑💻Usage (Development)
Start the server and test it with the MCP inspector
uv add "mcp[cli]"
mcp dev server.py
📚 Client Configuration
The following commands will generate a client configuration file, the location will depend on your operating system
uv add "mcp[cli]"
mcp install server.py --name "CyberChef API MCP Server"
Tip
After running the above command you can then tweak the client configuration to include the environment variable for the CyberChef API URL
{
"mcpServers": {
"CyberChef API MCP Server": {
"command": "uv",
"args": [
"run",
"--with",
"mcp[cli]",
"--directory",
"cyberchef-api-mcp-server/cyberchef_api_mcp_server/",
"mcp",
"run",
"server.py"
],
"env": {
"CYBERCHEF_API_URL": "your-cyberchef-api-url"
}
}
}
}
🔍 Demo
Using the MCP server in this example use case, the following prerequisites apply:
- You must have Claude desktop installed
- Have a running CyberChef API instance or one you are able to use
Here is a basic prompt being solved using the MCP server tools:
🙇 References
🪪 License
MIT License
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.