gotoHuman MCP Server

gotoHuman MCP Server bridges the gap between autonomous AI agents and human oversight. In many production environments, an AI may generate code, draft content, or propose decisions that still require a final human check before deployment. The gotoHuman server provides an asynchronous, API‑driven approval workflow that can be embedded directly into any MCP‑compatible client such as Claude, Cursor, or Windsurf. By exposing a set of tools that create, list, and manage review forms, the server turns any AI output into a structured request for human validation, ensuring that sensitive or critical tasks receive the necessary scrutiny.

The server’s core value lies in its fully‑managed, end‑to‑end approach. Developers no longer need to build custom notification systems or UI components for approvals; gotoHuman delivers a ready‑made, customizable interface that handles authentication, assignment, and status tracking. The built‑in webhook mechanism delivers approval results back to the originating workflow, allowing agents to continue processing only after a human has signed off. This tight integration eliminates manual handoffs and reduces the risk of errors slipping through automated pipelines.

Key capabilities include:

• Form Management – and let agents discover available review templates and retrieve their field definitions.
• Approval Requests – submits a payload that appears in the gotoHuman inbox, optionally assigning reviewers and attaching metadata.
• Team Collaboration – Built‑in auth and user assignment support multiple reviewers, facilitating peer review or managerial sign‑offs.
• Extensibility – The API can be extended with custom fields, notifications, and training data pipelines to improve future AI outputs.

Typical use cases span software engineering (code reviews), content creation (copy editing), compliance checks, and any scenario where human judgment is essential before final release. By inserting a single MCP call into an agent’s workflow, developers can enforce policy compliance, meet regulatory requirements, or simply add a safety net for high‑stakes decisions.

In practice, an AI assistant might generate a draft email, then invoke to send it to the designated editor. Once the editor approves or edits the content, a webhook triggers the agent to send the finalized email. This pattern scales effortlessly across teams and projects, making human‑in‑the‑loop a first‑class citizen in modern AI development pipelines.