Introduction to the MCP Reference

What is the Confident AI MCP server?

The Confident AI MCP server is the official Model Context Protocol server for Confident AI. It exposes your project as a set of tools a coding agent can call, so the agent works with your evaluation data from inside the editor instead of through the dashboard. It allows you to:

• Pull and version prompts and evaluation datasets
• Run cloud evaluations and read back test runs, metrics, and metric collections
• Browse production traces, threads, and spans, and evaluate any of them
• Manage human annotations, annotation queues, dashboards, and alerts
• Run red-teaming risk assessments and check projects against governance policy

Everything the server exposes is also available in the web UI and over the Confident API — think AWS console versus AWS CLI. Same resources, different interface.

Prerequisites

• A Confident AI account. If you don't have one, sign up here.
• An MCP client that supports remote servers and OAuth — Cursor, Claude Code, Claude Desktop, Windsurf, or anything else that speaks the Model Context Protocol

No API key is needed. Authentication is OAuth, and the server acts as you, across every project your account can reach.

Server URLs

Confident AI hosts the MCP server for you. Pick the URL for your region:

The examples below use the US URL. Swap in the EU URL if that's your region, or your own URL if you're self-hosting.

1. Add the server to your client

   The first time your client reaches the server it opens a browser window where you sign in to Confident AI and approve the connection. Your client stores the resulting token and refreshes it on its own, so you won't have to authenticate again.

   CursorClaude CodeClaude DesktopWindsurf

   Add the following to your .cursor/mcp.json file:

   {
     "mcpServers": {
       "Confident AI MCP": {
         "url": "https://mcp.confident-ai.com/mcp"
       }
     }
   }

   Then open Cursor Settings → MCP and hit Authenticate on the server to finish signing in through your browser.

2. Pick a project

   Every tool takes a required project_id — except list_projects, which is how your agent discovers the ids available to it, and the organization-wide governance tools.

   In practice you never type an id yourself. Ask your agent to work in a project by name, and it will call list_projects first, match the name, and reuse that id for the rest of the session:

   List my Confident AI projects, then pull the latest traces from the production one.

   list_projects returns each project's id, name, description, organization, and governance policy — enough for your agent to tell them apart, or to ask you which one you meant when the name is ambiguous.

Tools

The server groups its tools into areas, one per area of the platform — prompts, datasets, traces, dashboards, governance, and the rest. Each tool's description is written for the model that reads it, so an agent can usually pick the right one from the task alone.

Self-hosted deployments

If you run Confident AI inside your own cloud account, the MCP server ships with your deployment and talks to your instance rather than the hosted endpoint — your traces, prompts, and evaluation data never leave your infrastructure. Point your client at your deployment's /mcp URL in place of the hosted one; everything else on this page is unchanged.

Sign-in follows the same path. The MCP server advertises your own deployment's backend as its authorization server, so the OAuth flow runs entirely against your instance.

See self-hosting for how a self-hosted deployment is architected, and security and compliance for the full security model.

FAQs

Last updated on October 1, 2026