Private beta: AI SRE is currently in private beta and available to invited accounts only. To join the whitelist test, contact the Flashduty sales team to request access; features and the UI may change during the beta.
Overview
MCP (Model Context Protocol) lets AI SRE agents connect to external tools and data sources. Each MCP server is a standardized endpoint that exposes a set of tools (functions) — for example, querying GitHub issues, sending Slack messages, reading Kubernetes resources, or retrieving metrics from an observability platform. Once you configure and enable an MCP server in the console, agents will autonomously decide when they need an external capability during incident investigation and call the corresponding tool directly — no manual data-shuttling required.
What Is MCP
MCP (Model Context Protocol) is an open protocol that describes in a uniform way “what tools a server exposes, what parameters each tool accepts, and what it returns.” Think of it as the “USB port” of the AI agent world — any MCP-compliant server can be discovered and called plug-and-play by an agent, with no per-system adapter code required. In AI SRE, MCP extends an agent’s capability from “built-in tools” to “any external system”:
- You add an MCP server in the console (declaring its endpoint, transport, and authentication).
- At session start, enabled servers visible in the current scope become available to the agent.
- When needed, the agent discovers the tools that server provides and calls them directly.
Adding an MCP Server
Go to Plugins → MCP, click Add Server in the top-right corner, and fill in the form to define an MCP server.
Basic Fields
| Field | Type | Required | Description |
|---|---|---|---|
| Name | string | Yes | The server name, used as the identifier when agents call it (e.g., sqlite-explorer in mcp:sqlite-explorer/query). Must start with a letter; may only contain letters, digits, -, and _; length 1–255. Case-insensitive and unique within an account; cannot duplicate a built-in server name. |
| Transport | enum | Yes | How the agent communicates with the server. See “Transport” below. |
| Scope | Account / Team | Yes | The scope of this MCP server: Account (visible account-wide) or a specific Team (visible only to members of that team). See “Scope” below. |
| Description | string | Yes | Describes what this server does, for identification in the list. |
Each MCP server also has an AI description: after an agent first lists a server’s tools, the system automatically generates a capability summary from the tool list and displays it in the console. This summary refreshes automatically as the tool set changes — no manual maintenance needed.
Transport
| Transport | Use Case | Required Fields |
|---|---|---|
| HTTP Streaming (recommended) | Remote MCP server accessed via an HTTP endpoint | URL (endpoint), optional Headers (JSON) |
| SSE (standalone, legacy) | Legacy remote servers that only support Server-Sent Events | URL (endpoint), optional Headers (JSON) |
| stdio (local command) | MCP server launched as a local subprocess on the machine where the Runner runs | Command, arguments (one per line), environment variables (JSON) |
Authentication
MCP servers support three authentication modes that determine how credentials are provided to the server:For “Per-User API Key” and “Per-User OAuth”, if credentials are missing the agent’s tool call is paused and a credential entry or authorization dialog is shown to the current user; execution resumes once credentials are supplied. In Shared mode, no credentials are requested from the user.
Management & Inspection
The MCP list displays each server’s name (including its AI description; built-in servers are labeled with a “Built-in” badge), scope (account or team name), transport, an enabled toggle, and an actions column. The scope filter bar at the top lets you switch between All / Account / Team views.
Enable / Disable
Enable / Disable
Toggle the switch in the list. Only enabled servers are available to the agent; disabled servers are invisible to agents and cannot be called. Built-in servers are always enabled and their toggles cannot be changed.
Edit
Edit
Click the edit button (or click the row) to open the form. You can modify the name, transport, description, endpoint/command, authentication mode, and scope. If you do not have edit permission, the form opens in read-only mode with an explanation; built-in servers are likewise read-only.
Delete
Delete
Removes the MCP server from the current scope. Agents that depend on it will no longer be able to access its tools, and active sessions currently using it will fail. A confirmation prompt is shown before deletion.
Inspection: Viewing the Tools a Server Exposes
Which tools an MCP server exposes are discovered during a session by agents on demand — not displayed statically in the console. This is because the same MCP server may have different reachability and tool sets across different environments (Runners); connection state and tool count are per-environment properties, not global ones.Every account comes pre-configured with a built-in Flashduty MCP server (labeled “Built-in” in the list, read-only, always enabled), which lets agents read Flashduty incidents, alerts, and other data directly. It is maintained by the platform and requires no configuration on your part.
Scope
MCP shares the same two-level scope model as other resources (Skills, Knowledge Packs, Agents, Environments), divided into account level and team level:
| Scope | Visibility |
|---|---|
| Account-level | Visible to all members of the account |
| Team-level | Visible only to members of that team |
Related Pages
Skills
Skills call MCP-provided tools inside SKILL.md using the
mcp:server/tool notation.Agent
Agents share the same authentication modes and scope model as MCP.
BYOC
stdio MCP servers must run on a BYOC Runner; learn the difference between cloud Sandbox and self-hosted environments.
Console
Observe how agents inspect and invoke MCP tools during a session.
Flashduty MCP Server (Developer)
The opposite direction: the official MCP service for connecting Flashduty into third-party AI clients.