Dify Workflow MCP Server

The Gotoolkits MCP DifyWorkflow Server bridges the gap between conversational AI assistants and the powerful automation capabilities of the Dify platform. By exposing Dify workflows as first‑class tools through MCP, it lets agents invoke complex, multi‑step processes—such as translation pipelines, image generation or data extraction—directly from a user’s prompt. This eliminates the need for developers to build custom API wrappers or manage authentication flows, enabling a seamless “one‑click” integration between the assistant and any workflow already defined in Dify.

At its core, the server provides two essential commands: and . The former enumerates every workflow the configured API keys grant access to, giving agents a ready inventory of available actions. The latter accepts a workflow name and an input payload (defaulting to the field expected by most Dify workflows) and returns the workflow’s output. Because each workflow is tied to a unique API key, developers can expose only the functions they wish to share, maintaining strict access control while still offering rich functionality to end users.

Key capabilities include:

• On‑demand workflow execution: Trigger any authorized Dify workflow with a single tool call, no need to pre‑load or cache results.
• Multi‑workflow support: Configure a comma‑separated list of workflow names and corresponding API keys, allowing a single MCP instance to act as a portal for many independent automation pipelines.
• Dynamic prompt integration: Workflow names can be supplied directly in user prompts, enabling natural language commands like “Translate this message” or “Generate an image from the text.”
• Secure key management: API keys are supplied via environment variables, ensuring credentials never leak into logs or configuration files.

Real‑world scenarios that benefit from this server are plentiful. A customer support chatbot can automatically translate incoming tickets into the agent’s language, while a marketing assistant could generate branded images on demand. A data analyst might trigger a nightly ETL workflow that aggregates and cleans metrics, then feed the results back into an AI‑powered dashboard. In each case, the MCP server removes boilerplate code and lets developers focus on higher‑level logic rather than API plumbing.

Integrating the DifyWorkflow Server into an existing AI workflow is straightforward: once the server is running, any MCP‑compatible client—Claude, Gemini, or a custom agent—can discover its tools via the standard MCP discovery mechanism. The client can then prompt users to select a workflow, pass in contextual data, and receive the processed output as part of the conversation. Because the server adheres to MCP’s tool invocation protocol, it can be chained with other tools, enabling complex, multi‑step interactions that combine external APIs, local computation, and AI reasoning in a single coherent flow.

In summary, the Gotoolkits MCP DifyWorkflow Server empowers developers to expose sophisticated automation pipelines from the Dify platform as native AI tools. By handling authentication, workflow discovery, and execution behind a familiar MCP interface, it accelerates the creation of intelligent assistants that can orchestrate real‑world actions with minimal friction.