Overview of DV Flow MCP Server

The DV Flow MCP server provides a lightweight, standards‑compliant bridge between AI assistants and the DV Flow workflow engine. By exposing the core functionalities of DV Flow through the Model Context Protocol, it lets developers and AI agents orchestrate complex data pipelines without writing custom integration code. This solves the common problem of connecting language models to external workflow systems—something that is often error‑prone and requires manual API handling.

At its heart, the server translates MCP requests into DV Flow actions. A client can request a tool to start a workflow, query the status of an existing run, or retrieve artifacts produced by downstream steps. The server handles authentication, request validation, and response formatting, allowing the AI assistant to treat DV Flow as a first‑class tool. This abstraction is especially valuable for developers who want to embed data processing or machine learning pipelines directly into conversational agents, without exposing the intricacies of DV Flow’s REST API.

Key capabilities include:

• Resource management: Expose workflow definitions and artifact repositories as searchable resources that an AI can reference or list.
• Tool execution: Offer a tool endpoint that accepts workflow parameters, triggers executions, and streams progress back to the client.
• Prompt integration: Provide pre‑defined prompts that guide users in crafting workflow invocations, ensuring consistent input structures.
• Sampling and context handling: Allow the assistant to retrieve partial results or intermediate states, enabling iterative refinement of workflow parameters.

Real‑world scenarios span from automated data ingestion pipelines in finance to continuous training loops for machine learning models in healthcare. For instance, a conversational AI could ask a user for a dataset URL, trigger a DV Flow job that cleans and normalizes the data, and then return a ready‑to‑train model artifact—all within a single chat interaction.

Integration with existing AI workflows is seamless: the MCP server can be added as an additional tool in any Claude or GPT‑style agent, and because it follows the MCP specification, it can coexist with other MCP servers (e.g., file storage or database connectors) in a unified agent ecosystem. Its lightweight design means it can run on modest infrastructure, making it suitable for both cloud deployments and edge scenarios.