CyberChef API MCP Server

The CyberChef API MCP Server bridges the powerful data‑manipulation capabilities of the CyberChef ecosystem with modern AI assistants through the Model Context Protocol (MCP). By exposing a suite of resources and tools that mirror CyberChef’s operations, the server allows any MCP‑compatible language model to query available categories, list specific operations, and execute complex recipes without needing direct access to the CyberChef UI. This abstraction is especially valuable for developers building automated analysis pipelines or integrating data‑preprocessing steps into conversational agents.

At its core, the server offers three types of interactions: resources that provide contextual information about available categories and operations, and tools that perform actual data transformations. The resource delivers an up‑to‑date catalog of operation categories, enabling assistants to suggest relevant actions. The resource lists all operations within a chosen category, giving models fine‑grained insight into available techniques. The execution tools—, , and —allow models to run single or batch recipes, or invoke CyberChef’s automatic “magic” operation that heuristically decodes unknown data. This design mirrors the CyberChef user experience while keeping interactions lightweight and stateless.

Developers can leverage this server in a variety of real‑world scenarios. For instance, an AI assistant tasked with forensic analysis can request the “magic” tool to automatically detect encoding schemes in a hex dump, then construct and execute a recipe that decodes the data. In data‑engineering workflows, an assistant could fetch available transformations from a given category and compose a batch recipe to clean or enrich large datasets on the fly. Because the server communicates over MCP, it integrates seamlessly into existing AI pipelines that already consume MCP services—whether via the Claude desktop, a custom LLM client, or any tool that supports the protocol.

Unique advantages of this implementation stem from its tight coupling with CyberChef’s API and its minimal operational footprint. By running as a lightweight FastMCP service, it requires only an environment variable pointing to a CyberChef API instance, making deployment straightforward in cloud or on‑premise environments. The clear separation between context (resources) and action (tools) aligns with best practices for conversational AI, allowing models to ask “what can I do?” before deciding on a specific recipe. Overall, the CyberChef API MCP Server empowers developers to embed sophisticated data‑transformation logic into AI assistants without reinventing the wheel, fostering more intelligent, contextually aware automation across security, data science, and engineering domains.