awesome-copilot/skills/flowstudio-power-automate-build/SKILL.md

name, description

name

description

flowstudio-power-automate-build

Build, scaffold, and deploy Power Automate cloud flows using the FlowStudio MCP server. Your agent constructs flow definitions, wires connections, deploys, and tests — all via MCP without opening the portal. Load this skill when asked to: create a flow, build a new flow, deploy a flow definition, scaffold a Power Automate workflow, construct a flow JSON, update an existing flow's actions, patch a flow definition, add actions to a flow, wire up connections, or generate a workflow definition from scratch. Requires a FlowStudio MCP subscription — see https://mcp.flowstudio.app

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 flowstudio-power-automate-mcp skill for connection setup. Subscribe at https://mcp.flowstudio.app

Workflow:

1. Load current build tools.
2. Check for an existing flow.
3. Resolve connection references.
4. Build the definition.
5. Deploy.
6. Verify.
7. Test.

---

Source of Truth

Always call list_skills / tool_search first to confirm available tool names and parameter schemas. Tool names and parameters may change between server versions. This skill covers response shapes, behavioral notes, and build patterns — things tool schemas cannot tell you. If this document disagrees with tool_search 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

---

0. Load the Current Build Tools

For a brand-new flow, load the server's create-flow bundle. For editing an existing flow, load build-flow. This keeps the agent aligned with the MCP server's current schema before constructing JSON.

schemas = mcp("tool_search", query="skill:create-flow")
# Includes list_live_environments, list_live_connections,
# describe_live_connector, get_live_dynamic_options, update_live_flow.

If you need a tool outside the bundle, load it explicitly:

mcp("tool_search", query="select:get_live_dynamic_properties")

---

1. Safety Check: Does the Flow Already Exist?

Always look before you build to avoid duplicates:

results = mcp("list_live_flows",
    environmentName=ENV,
    mode="owner",
    search="My New Flow",
    top=20)

# list_live_flows returns { "flows": [...], "mode": "...", ... }
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

For very large environments, list_live_flows may return a continuation URL. Pass it back as continuationUrl with the same mode to retrieve the next batch. Use mode="admin" only when the user needs all environment flows and the MCP identity has admin rights.

---

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 — Find active connections

conns = mcp("list_live_connections", environmentName=ENV)
active = [c for c in conns["connections"]
          if c["statuses"][0]["status"] == "Connected"]
conn_map = {c["connectorName"]: c["id"] for c in active}

For a known connector, pass search to reduce output and get paste-ready connectionReferenceTemplate and hostTemplate values:

sp_conns = mcp("list_live_connections",
    environmentName=ENV,
    search="shared_sharepointonline")

2b — Determine which connectors the flow needs

Common connector API names: SharePoint shared_sharepointonline, Outlook shared_office365, Teams shared_teams, Approvals shared_approvals, OneDrive shared_onedriveforbusiness, Excel shared_excelonlinebusiness, Dataverse shared_commondataserviceforapps, Forms shared_microsoftforms.

Flows that need no connectors, such as Recurrence + Compose + HTTP only, can omit connectionReferences.

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 missing:
    # STOP: connections require browser OAuth consent.
    # Ask the user to create the missing connector connections in the
    # selected environment, then re-run list_live_connections.
    raise Exception(f"Missing active connections: {missing}")

2d — Build the connectionReferences block

connection_references = {}
host_templates = {}
for connector in connectors_needed:
    c = next(c for c in active if c["connectorName"] == connector)
    connection_references[connector] = c.get("connectionReferenceTemplate") or {
        "connectionName": c["id"],   # the connection id from list_live_connections
        "source": "Invoker",
        "id": f"/providers/Microsoft.PowerApps/apis/{connector}"
    }
    host_templates[connector] = c.get("hostTemplate") or {
        "connectionName": connector
    }

In Step 3 action JSON, inputs.host.connectionName must be the map key such as shared_teams, not the GUID. The GUID belongs only inside the connectionReferences[connector].connectionName value. If an existing flow uses the same connectors, you may also copy its properties.connectionReferences from get_live_flow.

---

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.

Discover connector operations before guessing JSON

For connector-backed triggers/actions, prefer the live connector describer over hand-written shapes. It can return authored hints, canonical examples, variant keys, inputs/outputs, and dynamic metadata pointers.

# Search across connectors when you know the user's intent but not the API.
matches = mcp("describe_live_connector",
    environmentName=ENV,
    search="send email",
    top=5)

# Describe a specific operation before copying an exampleDefinition.
op = mcp("describe_live_connector",
    environmentName=ENV,
    connectorName="shared_office365",
    operationId="SendEmailV2")
print(op.get("hint"))

When an operation has multiple authored variants, request the variant the flow needs:

teams_chat = mcp("describe_live_connector",
    environmentName=ENV,
    connectorName="shared_teams",
    operationId="PostMessageToConversation",
    variant="flowbot_chat")

When the operation description says a parameter has dynamic options or dynamic properties, call the indicated next tool:

sp_op = mcp("describe_live_connector",
    environmentName=ENV,
    connectorName="shared_sharepointonline",
    operationId="GetItems")

sites = mcp("get_live_dynamic_options",
    environmentName=ENV,
    connectorName="shared_sharepointonline",
    connectionName=conn_map["shared_sharepointonline"],
    operationId="GetItems",
    parameterName="dataset",
    dynamicMetadata=sp_op["dynamicParameters"]["dataset"])

fields = mcp("get_live_dynamic_properties",
    environmentName=ENV,
    connectorName="shared_sharepointonline",
    connectionName=conn_map["shared_sharepointonline"],
    operationId="GetItems",
    parameterName="item",
    parameters={"dataset": "<site-url>", "table": "<list-id>"},
    dynamicMetadata=sp_op["dynamicProperties"]["item"])

Use dynamic options for dropdown IDs such as SharePoint sites/lists and Teams teams/channels. Use dynamic properties for schema/field shapes such as SharePoint list item columns.

---

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:

definition["description"] = "Weekly SharePoint → Teams notification flow, built by agent"

result = mcp("update_live_flow",
    environmentName=ENV,
    # flowName omitted → creates a new flow
    definition=definition,
    connectionReferences=connection_references,
    displayName="Overdue Invoice Notifications"
)

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:

definition["description"] = (
    "Updated by agent on " + __import__('datetime').datetime.utcnow().isoformat()
)

result = mcp("update_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,
    definition=definition,
    connectionReferences=connection_references,
    displayName="My Updated Flow"
)

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.

⚠️ Flow description lives at definition["description"]. The current server appends #flowstudio-mcp for usage tracking. Do not pass a top-level description argument unless tool_search shows one in the active schema.

Common deployment errors

Error message (contains)	Cause	Fix
missing from connectionReferences	An action's host.connectionName references a key that doesn't exist in the connectionReferences map	Ensure host.connectionName uses the key from connectionReferences (e.g. shared_teams), not the raw GUID
ConnectionAuthorizationFailed / 403	The connection GUID belongs to another user or is not authorized	Re-run Step 2a and use a connection owned by the current x-api-key user
InvalidTemplate / InvalidDefinition	Syntax error in the definition JSON	Check runAfter chains, expression syntax, and action type spelling
ConnectionNotConfigured	A connector action exists but the connection GUID is invalid or expired	Re-check list_live_connections for a fresh GUID

---

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()))

---

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.

defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
triggers = defn["properties"]["definition"]["triggers"]
manual = next(iter(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.

Compact recipe:

production_trigger = definition["triggers"]
definition["triggers"] = {
    "manual": {"type": "Request", "kind": "Http", "inputs": {"schema": {}}}
}

result = mcp("update_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,       # omit if creating new
    definition=definition,
    connectionReferences=connection_references,
    displayName="Overdue Invoice Notifications")
FLOW_ID = FLOW_ID or result["created"]

test = mcp("trigger_live_flow", environmentName=ENV, flowName=FLOW_ID,
           body={"sample": "payload"})
runs = mcp("get_live_flow_runs", environmentName=ENV, flowName=FLOW_ID, top=1)

if runs[0]["status"] == "Failed":
    err = mcp("get_live_flow_run_error",
        environmentName=ENV, flowName=FLOW_ID, runName=runs[0]["name"])
    raise Exception(err["failedActions"][-1])

definition["triggers"] = production_trigger
mcp("update_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,
    definition=definition,
    connectionReferences=connection_references)

The trigger is only the entry point; testing through HTTP still exercises the same actions. If actions use triggerBody() or triggerOutputs(), pass a representative trigger_live_flow.body shaped like the production trigger payload.

---

Gotchas

Mistake	Consequence	Prevention
Missing connectionReferences in deploy	400 "Supply connectionReferences"	Always call list_live_connections first
"operationOptions" missing on Foreach	Parallel execution, race conditions on writes	Always add "Sequential"
union(old_data, new_data)	Old values override new (first-wins)	Use union(new_data, old_data)
split() on potentially-null string	InvalidTemplate crash	Wrap with coalesce(field, '')
Checking result["error"] exists	Always present; true error is != null	Use result.get("error") is not None
Flow deployed but state is "Stopped"	Flow won't run on schedule	Call set_live_flow_state with state: "Started" — do not use update_live_flow for state changes
Teams "Chat with Flow bot" recipient as object	400 GraphUserDetailNotFound	Use plain string with trailing semicolon (see below)
Copilot/Skills flow not in a solution	Copilot Studio may not discover it as an agent tool	After deploy, call add_live_flow_to_solution with the target solutionId
Button/Skills trigger used for MCP testing	MCP cannot directly fire the production trigger	Test the same actions through a temporary HTTP twin, then swap the trigger back
Connector action missing metadata.operationMetadataId	Designer/run-only UI can behave inconsistently	Preserve existing IDs; add stable GUIDs for new connector actions
Placeholder Excel scriptId	Dynamic validation fails at save time	Resolve the real Office Script ID before deploying
SharePoint PatchItem omits required fields	Save can fail even if the field is not changing	Echo unchanged required fields such as item/Title
Copilot Studio connector calls a draft agent	Connector invocation can fail or hit stale behavior	Publish the agent before testing/resubmitting the flow

Teams PostMessageToConversation — Recipient Formats

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

Location	body/recipient format	Example
Chat with Flow bot	Plain email string with trailing semicolon	"user@contoso.com;"
Channel	Object with groupId and channelId	{"groupId": "...", "channelId": "..."}

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

• flowstudio-power-automate-mcp — Core connection setup and tool reference
• flowstudio-power-automate-debug — Debug failing flows after deployment