Build & Deploy Power Automate Flows with FlowStudio MCP

Step-by-step guide for constructing and deploying Power Automate cloud flows

programmatically through the FlowStudio MCP server.

Prerequisite: A FlowStudio MCP server must be reachable with a valid JWT.

See the power-automate-mcp skill for connection setup.

Subscribe at https://mcp.flowstudio.app

Source of Truth

> Always call tools/list first to confirm available tool names and their

> parameter schemas. Tool names and parameters may change between server versions.

> This skill covers response shapes, behavioral notes, and build patterns —

> things tools/list cannot tell you. If this document disagrees with tools/list

> or a real API response, the API wins.

Python Helper

import json, urllib.request

MCP_URL   = "https://mcp.flowstudio.app/mcp"
MCP_TOKEN = "<YOUR_JWT_TOKEN>"

def mcp(tool, **kwargs):
    payload = json.dumps({"jsonrpc": "2.0", "id": 1, "method": "tools/call",
                          "params": {"name": tool, "arguments": kwargs}}).encode()
    req = urllib.request.Request(MCP_URL, data=payload,
        headers={"x-api-key": MCP_TOKEN, "Content-Type": "application/json",
                 "User-Agent": "FlowStudio-MCP/1.0"})
    try:
        resp = urllib.request.urlopen(req, timeout=120)
    except urllib.error.HTTPError as e:
        body = e.read().decode("utf-8", errors="replace")
        raise RuntimeError(f"MCP HTTP {e.code}: {body[:200]}") from e
    raw = json.loads(resp.read())
    if "error" in raw:
        raise RuntimeError(f"MCP error: {json.dumps(raw['error'])}")
    return json.loads(raw["result"]["content"][0]["text"])

ENV = "<environment-id>"  # e.g. Default-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

Step 1 — Safety Check: Does the Flow Already Exist?

Always look before you build to avoid duplicates:

results = mcp("list_live_flows", environmentName=ENV)

# list_live_flows returns { "flows": [...] }
matches = [f for f in results["flows"]
           if "My New Flow".lower() in f["displayName"].lower()]

if len(matches) > 0:
    # Flow exists — modify rather than create
    FLOW_ID = matches[0]["id"]   # plain UUID from list_live_flows
    print(f"Existing flow: {FLOW_ID}")
    defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
else:
    print("Flow not found — building from scratch")
    FLOW_ID = None

Step 2 — Obtain Connection References

Every connector action needs a connectionName that points to a key in the

flow's connectionReferences map. That key links to an authenticated connection

in the environment.

> MANDATORY: You MUST call list_live_connections first — do NOT ask the

> user for connection names or GUIDs. The API returns the exact values you need.

> Only prompt the user if the API confirms that required connections are missing.

2a — Always call list_live_connections first

conns = mcp("list_live_connections", environmentName=ENV)

# Filter to connected (authenticated) connections only
active = [c for c in conns["connections"]
          if c["statuses"][0]["status"] == "Connected"]

# Build a lookup: connectorName → connectionName (id)
conn_map = {}
for c in active:
    conn_map[c["connectorName"]] = c["id"]

print(f"Found {len(active)} active connections")
print("Available connectors:", list(conn_map.keys()))

2b — Determine which connectors the flow needs

Based on the flow you are building, identify which connectors are required.

Common connector API names:

> Flows that need NO connections (e.g. Recurrence + Compose + HTTP only)

> can skip the rest of Step 2 — omit connectionReferences from the deploy call.

2c — If connections are missing, guide the user

connectors_needed = ["shared_sharepointonline", "shared_office365"]  # adjust per flow

missing = [c for c in connectors_needed if c not in conn_map]

if not missing:
    print("✅ All required connections are available — proceeding to build")
else:
    # ── STOP: connections must be created interactively ──
    # Connections require OAuth consent in a browser — no API can create them.
    print("⚠️  The following connectors have no active connection in this environment:")
    for c in missing:
        friendly = c.replace("shared_", "").replace("onlinebusiness", " Online (Business)")
        print(f"   • {friendly}  (API name: {c})")
    print()
    print("Please create the missing connections:")
    print("  1. Open https://make.powerautomate.com/connections")
    print("  2. Select the correct environment from the top-right picker")
    print("  3. Click '+ New connection' for each missing connector listed above")
    print("  4. Sign in and authorize when prompted")
    print("  5. Tell me when done — I will re-check and continue building")
    # DO NOT proceed to Step 3 until the user confirms.
    # After user confirms, re-run Step 2a to refresh conn_map.

2d — Build the connectionReferences block

Only execute this after 2c confirms no missing connectors:

connection_references = {}
for connector in connectors_needed:
    connection_references[connector] = {
        "connectionName": conn_map[connector],   # the GUID from list_live_connections
        "source": "Invoker",
        "id": f"/providers/Microsoft.PowerApps/apis/{connector}"
    }

> IMPORTANT — host.connectionName in actions: When building actions in

> Step 3, set host.connectionName to the key from this map (e.g.

> shared_teams), NOT the connection GUID. The GUID only goes inside the

> connectionReferences entry. The engine matches the action's

> host.connectionName to the key to find the right connection.

> Alternative — if you already have a flow using the same connectors,

> you can extract connectionReferences from its definition:

> ```python

> ref_flow = mcp("get_live_flow", environmentName=ENV, flowName="")

> connection_references = ref_flow["properties"]["connectionReferences"]

> ```

See the power-automate-mcp skill's connection-references.md reference

for the full connection reference structure.

Step 3 — Build the Flow Definition

Construct the definition object. See flow-schema.md

for the full schema and these action pattern references for copy-paste templates:

• action-patterns-core.md — Variables, control flow, expressions
• action-patterns-data.md — Array transforms, HTTP, parsing
• action-patterns-connectors.md — SharePoint, Outlook, Teams, Approvals

definition = {
    "$schema": "https://schema.management.azure.com/providers/Microsoft.Logic/schemas/2016-06-01/workflowdefinition.json#",
    "contentVersion": "1.0.0.0",
    "triggers": { ... },   # see trigger-types.md / build-patterns.md
    "actions": { ... }     # see ACTION-PATTERNS-*.md / build-patterns.md
}

> See build-patterns.md for complete, ready-to-use

> flow definitions covering Recurrence+SharePoint+Teams, HTTP triggers, and more.

Step 3a — Resolving Dynamic Connector Values

When an action input needs a value picked from a connector dropdown (e.g. a

SharePoint list ID, a Dataverse table name, a user's Azure AD UPN), use

get_live_dynamic_options to resolve it via MCP rather than hardcoding GUIDs.

# Resolve a SharePoint list by site
opts = mcp("get_live_dynamic_options",
    environmentName=ENV,
    connectorName="shared_sharepointonline",
    operationId="GetTables",
    parameters={"dataset": "https://contoso.sharepoint.com/sites/HR"})
# opts["value"] → [{"Name": "<list-guid>", "DisplayName": "Employees"}, ...]

> Outer-parameter auto-bridge (server v1.1.6+): you can pass arbitrary outer

> parameters directly in parameters — the server now synthesizes the

> parameterReference mapping that PA's listEnum requires. Before 1.1.6 you had

> to declare dynamicMetadata.parameters: {paramName: {parameterReference: "name"}}

> manually or get IncorrectDynamicInvokeParameter. This makes it practical to

> invoke arbitrary connector operations through the dynamic-options pipeline

> (e.g. shared_office365users.SearchUserV2 for AAD user lookup).

AadGraph user-picker fallback

For Outlook actions like GetEmailsV3 (parameters mailboxAddress, to, cc,

from), PA's listEnum uses builtInOperation:AadGraph.GetUsers — which is

broken and returns DynamicListValuesUndefinedOrInvalid for every call.

describe_live_connector (v1.1.6+) detects these parameters and returns a

structured fallback field on each affected parameter pointing at a working

alternative. Use shared_office365users.SearchUserV2 to resolve the same

AAD user shape {value: [{id, displayName, mail, userPrincipalName, ...}]}:

# Borrow a shared_office365users connection (any active one will do)
conn = next(c for c in conn_map if "office365users" in c)

users = mcp("get_live_dynamic_options",
    environmentName=ENV,
    connectorName="shared_office365users",
    connectionName=conn_map[conn],   # see Step 2a
    operationId="SearchUserV2",
    parameters={"searchTerm": "john", "top": 10})
# users["value"] → [{"Id": "...", "DisplayName": "John Smith", "Mail": "..."}, ...]

Then plug the resolved Mail value into the Outlook action's parameter — no

need to call AadGraph.GetUsers directly.

Step 4 — Deploy (Create or Update)

update_live_flow handles both creation and updates in a single tool.

Create a new flow (no existing flow)

Omit flowName — the server generates a new GUID and creates via PUT:

result = mcp("update_live_flow",
    environmentName=ENV,
    # flowName omitted → creates a new flow
    definition=definition,
    connectionReferences=connection_references,
    displayName="Overdue Invoice Notifications",
    description="Weekly SharePoint → Teams notification flow, built by agent"
)

if result.get("error") is not None:
    print("Create failed:", result["error"])
else:
    # Capture the new flow ID for subsequent steps
    FLOW_ID = result["created"]
    print(f"✅ Flow created: {FLOW_ID}")

Update an existing flow

Provide flowName to PATCH:

result = mcp("update_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,
    definition=definition,
    connectionReferences=connection_references,
    displayName="My Updated Flow",
    description="Updated by agent on " + __import__('datetime').datetime.utcnow().isoformat()
)

if result.get("error") is not None:
    print("Update failed:", result["error"])
else:
    print("Update succeeded:", result)

> ⚠️ update_live_flow always returns an error key.

> null (Python None) means success — do not treat the presence of the key as failure.

>

> ⚠️ description is required for both create and update.

Common deployment errors

Step 5 — Verify the Deployment

check = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)

# Confirm state
print("State:", check["properties"]["state"])  # Should be "Started"
# If state is "Stopped", use set_live_flow_state — NOT update_live_flow
# mcp("set_live_flow_state", environmentName=ENV, flowName=FLOW_ID, state="Started")

# Confirm the action we added is there
acts = check["properties"]["definition"]["actions"]
print("Actions:", list(acts.keys()))

Step 6 — Test the Flow

> MANDATORY: Before triggering any test run, ask the user for confirmation.

> Running a flow has real side effects — it may send emails, post Teams messages,

> write to SharePoint, start approvals, or call external APIs. Explain what the

> flow will do and wait for explicit approval before calling trigger_live_flow

> or resubmit_live_flow_run.

Updated flows (have prior runs) — ANY trigger type

> Use resubmit_live_flow_run first. It works for EVERY trigger type —

> Recurrence, SharePoint, connector webhooks, Button, and HTTP. It replays

> the original trigger payload. Do NOT ask the user to manually trigger the

> flow or wait for the next scheduled run.

runs = mcp("get_live_flow_runs", environmentName=ENV, flowName=FLOW_ID, top=1)
if runs:
    # Works for Recurrence, SharePoint, connector triggers — not just HTTP
    result = mcp("resubmit_live_flow_run",
        environmentName=ENV, flowName=FLOW_ID, runName=runs[0]["name"])
    print(result)   # {"resubmitted": true, "triggerName": "..."}

HTTP-triggered flows — custom test payload

Only use trigger_live_flow when you need to send a different payload

than the original run. For verifying a fix, resubmit_live_flow_run is

better because it uses the exact data that caused the failure.

# Read the request schema directly from the flow definition
defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
manual = next(iter(defn["properties"]["definition"]["triggers"].values()))
print("Expected body:", manual.get("inputs", {}).get("schema"))

result = mcp("trigger_live_flow",
    environmentName=ENV, flowName=FLOW_ID,
    body={"name": "Test", "value": 1})
print(f"Status: {result['responseStatus']}")

Brand-new non-HTTP flows (Recurrence, connector triggers, etc.)

A brand-new Recurrence or connector-triggered flow has no prior runs to

resubmit and no HTTP endpoint to call. This is the ONLY scenario where you

need the temporary HTTP trigger approach below. **Deploy with a temporary

HTTP trigger first, test the actions, then swap to the production trigger.**

7a — Save the real trigger, deploy with a temporary HTTP trigger

# Save the production trigger you built in Step 3
production_trigger = definition["triggers"]

# Replace with a temporary HTTP trigger
definition["triggers"] = {
    "manual": {
        "type": "Request",
        "kind": "Http",
        "inputs": {
            "schema": {}
        }
    }
}

# Deploy (create or update) with the temp trigger
result = mcp("update_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,       # omit if creating new
    definition=definition,
    connectionReferences=connection_references,
    displayName="Overdue Invoice Notifications",
    description="Deployed with temp HTTP trigger for testing")

if result.get("error") is not None:
    print("Deploy failed:", result["error"])
else:
    if not FLOW_ID:
        FLOW_ID = result["created"]
    print(f"✅ Deployed with temp HTTP trigger: {FLOW_ID}")

7b — Fire the flow and check the result

# Trigger the flow
test = mcp("trigger_live_flow",
    environmentName=ENV, flowName=FLOW_ID)
print(f"Trigger response status: {test['status']}")

# Wait for the run to complete
import time; time.sleep(15)

# Check the run result
runs = mcp("get_live_flow_runs",
    environmentName=ENV, flowName=FLOW_ID, top=1)
run = runs[0]
print(f"Run {run['name']}: {run['status']}")

if run["status"] == "Failed":
    err = mcp("get_live_flow_run_error",
        environmentName=ENV, flowName=FLOW_ID, runName=run["name"])
    root = err["failedActions"][-1]
    print(f"Root cause: {root['actionName']} → {root.get('code')}")
    # Debug and fix the definition before proceeding
    # See power-automate-debug skill for full diagnosis workflow

7c — Swap to the production trigger

Once the test run succeeds, replace the temporary HTTP trigger with the real one:

# Restore the production trigger
definition["triggers"] = production_trigger

result = mcp("update_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,
    definition=definition,
    connectionReferences=connection_references,
    description="Swapped to production trigger after successful test")

if result.get("error") is not None:
    print("Trigger swap failed:", result["error"])
else:
    print("✅ Production trigger deployed — flow is live")

> Why this works: The trigger is just the entry point — the actions are

> identical regardless of how the flow starts. Testing via HTTP trigger

> exercises all the same Compose, SharePoint, Teams, etc. actions.

>

> Connector triggers (e.g. "When an item is created in SharePoint"):

> If actions reference triggerBody() or triggerOutputs(), pass a

> representative test payload in trigger_live_flow's body parameter

> that matches the shape the connector trigger would produce.

Gotchas

Teams PostMessageToConversation — Recipient Formats

The body/recipient parameter format depends on the location value:

> Common mistake: passing {"to": "user@contoso.com"} for "Chat with Flow bot"

> returns a 400 GraphUserDetailNotFound error. The API expects a plain string.

Reference Files

• flow-schema.md — Full flow definition JSON schema
• trigger-types.md — Trigger type templates
• action-patterns-core.md — Variables, control flow, expressions
• action-patterns-data.md — Array transforms, HTTP, parsing
• action-patterns-connectors.md — SharePoint, Outlook, Teams, Approvals
• build-patterns.md — Complete flow definition templates (Recurrence+SP+Teams, HTTP trigger)

Related Skills

• power-automate-mcp — Foundation skill: connection setup, MCP helper, tool discovery
• power-automate-debug — Debug failing flows after deployment