Flyder MCP Server

MCP Server Flyder

The Flyder MCP server bridges the gap between AI assistants and the Flyder workflow platform, enabling developers to list and execute complex, pre‑built pipelines directly from within their conversational agents. By exposing Flyder’s workflow API through the Model Context Protocol, Claude (or any MCP‑compatible client) can treat each Flyder workflow as a first‑class tool, dramatically reducing the friction of integrating external automation into AI workflows.

At its core, the server offers two simple yet powerful commands. The tool retrieves a user‑specific catalog of workflow names and unique identifiers, allowing an assistant to present options or search for a particular task. The tool triggers execution of any workflow by its ID, optionally passing in custom text input. The response contains the workflow’s output object, giving the assistant immediate access to results such as generated reports, transformed data, or downstream actions. This tight coupling means developers can orchestrate multi‑step processes—data ingestion, transformation, and notification—in a single conversational turn.

Key capabilities include:

• Seamless authentication via environment variables (, ), keeping secrets out of the conversation flow.
• Dynamic workflow discovery so assistants can adapt to a user’s evolving Flyder library without hard‑coding IDs.
• Optional input injection, enabling context‑aware workflow runs that incorporate user prompts or intermediate AI outputs.
• Early‑development flexibility, with the server’s API subject to refinement, encouraging community feedback and rapid iteration.

Real‑world scenarios that benefit from this integration are plentiful. A customer support bot could invoke a Flyder workflow to pull ticket data, run sentiment analysis, and generate an email summary—all triggered by a single user request. A data analyst might ask the assistant to execute a complex ETL pipeline on demand, receiving the processed dataset as part of the chat. In DevOps, an AI assistant could launch a deployment workflow in Flyder to spin up environments or run automated tests.

Integrating the server into an AI workflow is straightforward: add a single entry to the , and the assistant automatically gains access to the two tools. From there, developers can compose higher‑level macros or chain multiple tool calls, leveraging Flyder’s rich workflow ecosystem without leaving the conversational interface. The result is a powerful, low‑code bridge that empowers developers to harness external automation directly within AI assistants.