HiveFlow MCP Server – Bridging AI Assistants and Automation

The HiveFlow MCP server solves the friction that developers face when trying to embed powerful automation workflows into conversational AI experiences. By exposing HiveFlow’s full API surface through the Model Context Protocol, an assistant such as Claude or Cursor can create, list, and execute flows with the same natural‑language commands it uses to answer questions. This eliminates the need for custom SDKs or webhooks, letting teams focus on business logic rather than integration plumbing.

At its core, the server acts as a lightweight, stateless gateway: AI assistants send MCP requests → the server forwards them over HTTPS to HiveFlow’s REST endpoints → responses are returned as structured resources. This architecture keeps all authentication and business logic inside HiveFlow while keeping the MCP server minimal and secure. Because the server never stores data locally, it can be deployed in any environment—cloud, on‑prem, or as a local development proxy—without exposing sensitive information.

Key capabilities include:

• Flow Management Tools – Create, list, retrieve, execute, pause, and resume automation flows directly from the assistant.
• Execution History Access – Query past runs with to provide audit trails or troubleshooting context.
• MCP Server Registry – Tools for listing and registering other MCP servers, enabling multi‑tenant or hybrid deployments.
• Resource Paths – Structured URLs such as give the assistant read‑only access to flow definitions, allowing it to introspect and explain available automations.

Typical use cases span a wide spectrum: an HR bot that triggers onboarding workflows, a customer support assistant that pulls ticket‑handling flows, or a data analyst who can launch ETL pipelines on demand. In each scenario the assistant can ask, “Run the compliance audit flow with these parameters,” and the MCP server translates that into a secure API call, returning real‑time status updates.

Integration is straightforward for developers familiar with MCP: add the server to the client’s configuration, provide the HiveFlow API key and URL, and the assistant automatically discovers the new tools and resources. The server’s design emphasizes minimal configuration, HTTPS security, and no local persistence, making it a low‑overhead addition to any AI workflow that requires orchestrated actions or stateful processes.