# Agents over MCP

Fluksio exposes flow tools over MCP. An agent can inspect, edit, publish,
and run flows with the permissions of the user who authorizes it.

## Connect an agent

Set `MCP_ENABLED=true` in the engine's environment and restart it. The Python
package defaults to off; the Docker template enables it.

Configure your MCP client with an HTTP endpoint:

- Local Docker stack: `http://api.localhost/mcp`
- Deployment: `https://api.<your-domain>/mcp`

The client registers, opens browser authorization, and receives an OAuth
token after you approve access. The issuer and browser redirects use
`DOMAIN`, `ENVIRONMENT`, and `FRONTEND_HOST`, so configure those for the
addresses your client and browser can reach.

Manage registered clients under **Admin → Agents**. Revoking a client
invalidates its access. MCP uses a separate signing key at
`OAUTH_PRIVATE_KEY_FILE`; rotating that key invalidates all MCP tokens.

An instance connected to a [portal](../interface/portal.md) also answers
through it, at `https://hub.fluksio.com/i/<instance-id>/mcp` (the portal's
own `hub.` host for a custom one). The portal forwards the endpoint and its
OAuth, and the instance checks the agent's token as it does locally. The
consent page opens in the instance as the portal serves it, so open the
instance from the portal first if this browser has not recently.

## Available tools

| Task | Tools |
|---|---|
| Inspect flows | `list_flows`, `get_flow`, `get_node_source`, `list_node_types`, `get_graph`, `list_shared_nodes` |
| Inspect values | `get_flow_state`, `get_message_history` |
| Inspect the instance | `list_secrets` (names only), `get_modules`, `get_health`, `get_metrics`, `list_failures`, `list_runs` (live cascades) |
| Edit flows | `save_flow`, `save_node_source`, `validate_flow`, `publish_flow`, `discard_draft`, `delete_flow` |
| Operate flows | `run_flow`, `trigger_node`, `start_flow`, `stop_flow`, `pause_flow`, `resume_flow`, `cancel_node` |
| Install node packages | `apply_modules` |

Tools call the same API as the browser. Drafts, validation, version checks,
and superuser restrictions apply. Secrets cannot be read back.

A useful workflow: ask the agent to inspect the flow, save a draft, and
validate it. Review the draft before asking it to publish. The agent has
a publish tool, so make your intent explicit.

## The assistant

A superuser configures an OpenAI-compatible provider under
**Settings → Assistant**, with a base URL, API key, and model. This enables
the dashboard's Assistant entry for signed-in users.

The assistant uses the same tools with your own permissions. Draft edits
appear in the editor, and each answer renders as markdown with a tool trace.
Conversations are stored per user on the instance; the bin button clears
yours.
`MCP_ENABLED` is only needed for external clients, so the built-in assistant
works without it.

## Control

A live [Wren node](../reference/node-types.md#wren) with both `prompt` and
`actions` wired adds a **Control** tab to the chat. It uses the node's local
model and needs no assistant provider.

Choose a node, type a command, and inspect the reported actions: target
message, value, confidence, and whether each action was applied.
The node's `apply` and `min_confidence` settings decide whether it writes
to devices. Control conversations stay in the browser and clear on reload.