Agent Setup & Agentic Tutorial (MCP)

1. Connect your AI client (30 seconds)

Point your AI client at Glow’s hosted Streamable HTTP MCP server:

• MCP Server URL: https://api.getglow.ai/mcp.
• Authentication: Sign in with OAuth 2.0 when prompted, or create a scoped token in Your account → MCP using the Build drafts (Recommended) preset.
• Client config snippets: Open Connecting AI Apps & Editors for ready-to-copy setups for Claude, ChatGPT, Cursor, VS Code, and other MCP clients.

2. Paste this starter prompt (30 seconds)

Copy either prompt below into your connected AI client:

Starter Prompt A: Morning Focus Note (no external app accounts needed)

Read https://docs.getglow.ai/getting-started/agent-setup.md first. Using Glow MCP (list_workspaces, search_components, get_step_context, update_workflow, and validate_workflow), create a new Draft workflow named "Morning Focus Note" in my workspace:
1. Step 1: Scheduler trigger (schedule) set to every weekday at 09:00.
2. Step 2: AI Prompt (glowAi) connected from Step 1 that writes a one-sentence focus note for the day.
Leave the workflow in Draft, run validate_workflow to confirm valid: true, and return the canvasMarkdownLink without running it.

Starter Prompt B: Webhook Lead Triage with Conditions (branching)

Read https://docs.getglow.ai/getting-started/agent-setup.md first. Using Glow MCP (search_components, get_step_context, update_workflow, and validate_workflow), create a new Draft workflow named "Inbound Lead Triage" in my workspace:
1. Step 1: Webhook trigger (http/new-requests) with sample testing data {"email": "ada@acme.io", "company": "Acme", "notes": "Looking for enterprise rollout for 120 seats"}.
2. Step 2: AI Prompt (glowAi) with JSON output returning {"score": 85, "summary": "..."} based on {{ 1.body.notes }}.
3. Step 3: Conditions (condition) with a "Hot Lead" route checking {{ 2.result.score }} greater than or equal to 70 and an Else route.
Leave it in Draft, run validate_workflow, and return the canvasMarkdownLink without running a live test.

3. Open your live canvas and work both ways (1 minute)

Your agent replies with an Open in Glow link (canvasMarkdownLink) and a validation check (valid: true, errorCount: 0):

1. Click canvasMarkdownLink in your chat to open the Draft on the Glow multiplayer canvas.
2. Click any Step on the canvas to open its settings drawer. Tell your agent “update the Step I have open”, and it detects your selection (viewerSelection) and edits it in place.
3. Press N on the canvas to add a sticky note. Write what you want to change next and ask your agent to apply it.
4. Click Run on the canvas (or ask your agent to run run_workflow_test with writeMode: "preview") to inspect each Step’s output.

1. Confirm the workspace and balance

Call list_workspaces and list_workflows first. Confirm the target teamId, check the workspace balance, and verify whether to create a new Draft or update an existing one.

2. Inspect schemas and connected accounts

Call search_components to locate the exact componentKey (<app_slug>/<action> or a built-in key like glowAi). Next, call get_step_context to read live parameter schemas, connected accountId values, and remote picker choices before setting parameters.

3. Build in Draft with update_workflow

Call update_workflow to apply up to 50 canvas operations in one transaction. Omit workflowId and pass name plus a fresh idempotencyKey to create and wire a new Draft workflow in one call. Check viewerSelection from get_workflow whenever the user refers to “this step”.

4. Validate the graph with validate_workflow

Call validate_workflow after saving edits. Verify valid: true (errorCount: 0), connected edges, and valid output references before reporting the automation ready.