Docs/AI Clients (MCP)

AI Clients (MCP)

DBConvert Streams ships an MCP (Model Context Protocol) server so an AI client outside the app — Claude Desktop, Cursor, VS Code Copilot, and other MCP-compatible tools — can inspect your databases, schemas, files, and stream state through the same workspace you already use. It reads from your existing connections and never writes. Use it to add live DBConvert context to an AI app or IDE you already use.

Just want to chat with AI inside DBConvert? Use the built-in AI Chat. It needs no MCP registration, but does use one supported agent CLI installed on your computer. This section is for connecting an external AI client or a remote (HTTP) deployment.

What it gives you

Without MCP, you copy-paste schemas, error messages, and query results into chat windows — and they go stale the moment something changes. With MCP, your AI client calls DBConvert Streams directly and always works from live state: the same connections, schemas, files, and stream status you see in the app.

Because it reads through the workspace that already powers Data Explorer, there's no second set of database credentials and no second copy of your schema to keep in sync.

Quickstart

Pick the path that matches how your client runs:

  • External desktop AI client → stdio: open the AI Clients panel, click Connect next to your client, and restart it.
  • Docker (local or remote) → HTTP: point your client at http(s)://<host>/mcp with your API key — see Connect a remote AI client.

Then ask your client List my DBConvert connections. — if the connections you see in Data Explorer come back, you're done.

→ Full step-by-step, screenshots, and Docker setup: Setup.

No connections yet? An empty list back means MCP is wired up correctly — there's just nothing to show. Add one in the Data Explorer sidebar (+ New Connection) and ask again; see Connection management.

What you can ask

External MCP clients can use the full toolset for database inspection, analytics, federated queries across sources, query review, stream troubleshooting, and cross-source validation. Built-in AI Chat exposes the read-only tools relevant to its current scope. See AI Chat examples and the full tools reference.

What it cannot do

The MCP server is read-only — it inspects and advises, it never writes:

  • No data or schema changes — no INSERT / UPDATE / DELETE, no DROP / ALTER / CREATE. Only SELECT and WITH … SELECT pass the safety filter; everything else is rejected at the server before it reaches your database.
  • No connection or credential changes — connections are read from your workspace, never edited.
  • No stream control in this version — the AI can't start, stop, pause, resume, or reset streams yet. It surfaces status, errors, and the fix; you run the action.

When the AI suggests a change, you (or DBConvert Streams via the UI) run it. See Safety & privacy for the full boundary.

How it works

On the desktop app, the MCP server runs as a small process alongside DBConvert Streams; your AI client starts it on demand and talks to it over standard input/output (stdio). On a Docker or server deployment it runs as a service in your own stack and your client reaches it over HTTP. Either way it runs on your infrastructure and only ever responds to your client's read-only requests — it never calls out to DBConvert or your AI provider on its own.

⚡ No restart needed. Edit a connection in the DBConvert Streams UI and the MCP server picks up the change in a fraction of a second — no reconnecting your AI client.

In this section

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