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version: "1.0.1" name: flowstudio-power-automate-build description: >- 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:

Load current build tools.
Check for an existing flow.
Resolve connection references.
Build the definition.
Deploy.
Verify.
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

python

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.

python

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:

python

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:

python

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

python

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:

python

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

python

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

python

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

python

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.

python

# 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:

python

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:

python

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:

python

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:

python

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

python

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.

python

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.

python

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:

python

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

← v1.0.0 All versions

Skill v1.0.1

Build & Deploy Power Automate Flows with FlowStudio MCP

Source of Truth

Python Helper

0. Load the Current Build Tools

1. Safety Check: Does the Flow Already Exist?

2. Obtain Connection References

2a — Find active connections

2b — Determine which connectors the flow needs

2c — If connections are missing, guide the user

2d — Build the connectionReferences block

3. Build the Flow Definition

Discover connector operations before guessing JSON

4. Deploy (Create or Update)

Create a new flow (no existing flow)

Update an existing flow

Common deployment errors

5. Verify the Deployment

6. Test the Flow

Updated flows (have prior runs) — ANY trigger type

HTTP-triggered flows — custom test payload

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

Gotchas

Teams PostMessageToConversation — Recipient Formats

Reference Files

Related Skills

Teams `PostMessageToConversation` — Recipient Formats