flowstudio-power-automate-build（Flowstudio 强大的自动化构建功能）

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="<existing-flow-id>") 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.

schema = mcp("get_live_flow_http_schema",
    environmentName=ENV, flowName=FLOW_ID)
print("Expected body:", schema.get("requestSchema"))

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