Power Automate Debugging with FlowStudio MCP

A step-by-step diagnostic process for investigating failing Power Automate cloud flows through the FlowStudio MCP server.

Real debugging examples: Expression error in child flow | Data entry, not a flow bug | Null value crashes child flow

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

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 diagnostic 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

Step 1 — Locate the Flow

result = mcp("list_live_flows", environmentName=ENV)
# Returns a wrapper object: {mode, flows, totalCount, error}
target = next(f for f in result["flows"] if "My Flow Name" in f["displayName"])
FLOW_ID = target["id"]   # plain UUID — use directly as flowName
print(FLOW_ID)

Step 2 — Find the Failing Run

runs = mcp("get_live_flow_runs", environmentName=ENV, flowName=FLOW_ID, top=5)
# Returns direct array (newest first):
# [{"name": "08584296068667933411438594643CU15",
#   "status": "Failed",
#   "startTime": "2026-02-25T06:13:38.6910688Z",
#   "endTime": "2026-02-25T06:15:24.1995008Z",
#   "triggerName": "manual",
#   "error": {"code": "ActionFailed", "message": "An action failed..."}},
#  {"name": "...", "status": "Succeeded", "error": null, ...}]

for r in runs:
    print(r["name"], r["status"], r["startTime"])

RUN_ID = next(r["name"] for r in runs if r["status"] == "Failed")

Step 3 — Get the Top-Level Error

CRITICAL: get_live_flow_run_error tells you which action failed. get_live_flow_run_action_outputs tells you why. You must call BOTH. Never stop at the error alone — error codes like ActionFailed, NotSpecified, and InternalServerError are generic wrappers. The actual root cause (wrong field, null value, HTTP 500 body, stack trace) is only visible in the action's inputs and outputs.

err = mcp("get_live_flow_run_error",
    environmentName=ENV, flowName=FLOW_ID, runName=RUN_ID)
# Returns:
# {
#   "runName": "08584296068667933411438594643CU15",
#   "failedActions": [
#     {"actionName": "Apply_to_each_prepare_workers", "status": "Failed",
#      "error": {"code": "ActionFailed", "message": "An action failed..."},
#      "startTime": "...", "endTime": "..."},
#     {"actionName": "HTTP_find_AD_User_by_Name", "status": "Failed",
#      "code": "NotSpecified", "startTime": "...", "endTime": "..."}
#   ],
#   "allActions": [
#     {"actionName": "Apply_to_each", "status": "Skipped"},
#     {"actionName": "Compose_WeekEnd", "status": "Succeeded"},
#     ...
#   ]
# }

# failedActions is ordered outer-to-inner. The ROOT cause is the LAST entry:
root = err["failedActions"][-1]
print(f"Root action: {root['actionName']} → code: {root.get('code')}")

# allActions shows every action's status — useful for spotting what was Skipped
# See common-errors.md to decode the error code.

Step 4 — Inspect the Failing Action's Inputs and Outputs

This is the most important step. get_live_flow_run_error only gives you a generic error code. The actual error detail — HTTP status codes, response bodies, stack traces, null values — lives in the action's runtime inputs and outputs. Always inspect the failing action immediately after identifying it.

# Get the root failing action's full inputs and outputs
root_action = err["failedActions"][-1]["actionName"]
detail = mcp("get_live_flow_run_action_outputs",
    environmentName=ENV,
    flowName=FLOW_ID,
    runName=RUN_ID,
    actionName=root_action)

if len(detail) > 1:
    print(f"{root_action} returned {len(detail)} repetitions; inspect iteration indexes")
out = detail[0] if detail else {}
print(f"Action: {out.get('actionName')}")
print(f"Status: {out.get('status')}")

# For HTTP actions, the real error is in outputs.body
if isinstance(out.get("outputs"), dict):
    status_code = out["outputs"].get("statusCode")
    body = out["outputs"].get("body", {})
    print(f"HTTP {status_code}")
    print(json.dumps(body, indent=2)[:500])

    # Error bodies are often nested JSON strings — parse them
    if isinstance(body, dict) and "error" in body:
        err_detail = body["error"]
        if isinstance(err_detail, str):
            err_detail = json.loads(err_detail)
        print(f"Error: {err_detail.get('message', err_detail)}")

# For expression errors, the error is in the error field
if out.get("error"):
    print(f"Error: {out['error']}")

# Also check inputs — they show what expression/URL/body was used
if out.get("inputs"):
    print(f"Inputs: {json.dumps(out['inputs'], indent=2)[:500]}")

What the action outputs reveal (that error codes don't)

Foreach iterations

When actionName refers to an action inside a foreach, the output tool can return every repetition of that action. Each item may include repetitionIndexes with the loop name and zero-based itemIndex. Use iterationIndex to inspect one iteration after you find the suspicious item:

all_reps = mcp("get_live_flow_run_action_outputs",
    environmentName=ENV,
    flowName=FLOW_ID,
    runName=RUN_ID,
    actionName=root_action)

for rep in all_reps[:10]:
    print(rep.get("repetitionIndexes"), rep.get("status"), rep.get("error"))

one_rep = mcp("get_live_flow_run_action_outputs",
    environmentName=ENV,
    flowName=FLOW_ID,
    runName=RUN_ID,
    actionName=root_action,
    iterationIndex=3)

Evidence Compose Bookends

For uncertain connector work, add a Compose_*_Request before the risky action and a Compose_*_Result after it, with the result action allowed on both Succeeded and Failed. This gives future debugging a clean payload snapshot without requiring another deploy. Do not include secrets or long binary payloads in these bookends.

Example: HTTP action returning 500

Error code: "InternalServerError" ← this tells you nothing

Action outputs reveal:
  HTTP 500
  body: {"error": "Cannot read properties of undefined (reading 'toLowerCase')
    at getClientParamsFromConnectionString (storage.js:20)"}
  ← THIS tells you the Azure Function crashed because a connection string is undefined

Example: Expression error on null

Error code: "BadRequest" ← generic

Action outputs reveal:
  inputs: "body('HTTP_GetTokenFromStore')?['token']?['access_token']"
  outputs: ""   ← empty string, the path resolved to null
  ← THIS tells you the response shape changed — token is at body.access_token, not body.token.access_token

Step 5 — Read the Flow Definition

defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
actions = defn["properties"]["definition"]["actions"]
print(list(actions.keys()))

Find the failing action in the definition. Inspect its inputs expression to understand what data it expects.

Step 6 — Walk Back from the Failure

When the failing action's inputs reference upstream actions, inspect those too. Walk backward through the chain until you find the source of the bad data:

# Inspect multiple actions leading up to the failure
for action_name in [root_action, "Compose_WeekEnd", "HTTP_Get_Data"]:
    result = mcp("get_live_flow_run_action_outputs",
        environmentName=ENV,
        flowName=FLOW_ID,
        runName=RUN_ID,
        actionName=action_name)
    out = result[0] if result else {}
    print(f"\n--- {action_name} ({out.get('status')}) ---")
    print(f"Inputs:  {json.dumps(out.get('inputs', ''), indent=2)[:300]}")
    print(f"Outputs: {json.dumps(out.get('outputs', ''), indent=2)[:300]}")

⚠️ Output payloads from array-processing actions can be very large. Always slice (e.g. [:500]) before printing.

Tip: Omit actionName to list top-level actions when you're not sure which action produced the bad data. Once you pick an action inside a foreach, pass iterationIndex to avoid pulling every repetition into context.

Step 7 — Pinpoint the Root Cause

Expression Errors (e.g. split on null)

If the error mentions InvalidTemplate or a function name:

1. Find the action in the definition
2. Check what upstream action/expression it reads
3. Inspect that upstream action's output for null / missing fields

# Example: action uses split(item()?['Name'], ' ')
# → null Name in the source data
result = mcp("get_live_flow_run_action_outputs", ..., actionName="Compose_Names")
if not result:
    print("No outputs returned for Compose_Names")
    names = []
else:
    names = result[0].get("outputs", {}).get("body") or []
nulls = [x for x in names if x.get("Name") is None]
print(f"{len(nulls)} records with null Name")

Wrong Field Path

Expression triggerBody()?['fieldName'] returns null → fieldName is wrong. Inspect the trigger output to see the actual field names:

result = mcp("get_live_flow_run_action_outputs", ..., actionName="<trigger-action-name>")
print(json.dumps(result[0].get("outputs"), indent=2)[:500])

HTTP Actions Returning Errors

The error code says InternalServerError or NotSpecified — always inspect the action outputs to get the actual HTTP status and response body:

result = mcp("get_live_flow_run_action_outputs", ..., actionName="HTTP_Get_Data")
out = result[0]
print(f"HTTP {out['outputs']['statusCode']}")
print(json.dumps(out['outputs']['body'], indent=2)[:500])

Connection / Auth Failures

Look for ConnectionAuthorizationFailed — the connection owner must match the service account running the flow. Cannot fix via API; fix in PA designer.

Outlook user-picker failures (DynamicListValuesUndefinedOrInvalid)

Outlook actions like GetEmailsV3 use parameters (mailboxAddress, to, cc, from) whose dropdown is backed by builtInOperation:AadGraph.GetUsers — which is broken at the PA listEnum layer and always returns DynamicListValuesUndefinedOrInvalid. This shows up when an agent rebuilds or modifies an Outlook action via update_live_flow and tries to resolve a user through dynamic options. Don't fix it by retrying AadGraph — switch to shared_office365users.SearchUserV2 instead (returns the same AAD user shape). Use describe_live_connector to confirm whether the affected parameter exposes a structured fallback, then call get_live_dynamic_options against shared_office365users.SearchUserV2 instead of the broken AadGraph operation. For dynamic field schemas rather than dropdown options, use get_live_dynamic_properties with the metadata returned by describe_live_connector.

Step 8 — Apply the Fix

For expression/data issues:

defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
acts = defn["properties"]["definition"]["actions"]

# Example: fix split on potentially-null Name
acts["Compose_Names"]["inputs"] = \
    "@coalesce(item()?['Name'], 'Unknown')"

conn_refs = defn["properties"]["connectionReferences"]
result = mcp("update_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,
    definition=defn["properties"]["definition"],
    connectionReferences=conn_refs)

print(result.get("error"))  # None = success

⚠️ update_live_flow always returns an error key. A value of null (Python None) means success.

Step 9 — Verify the Fix

Use `resubmit_live_flow_run` to test ANY flow — not just HTTP triggers. resubmit_live_flow_run replays a previous run using its original trigger payload. This works for every trigger type: Recurrence, SharePoint "When an item is created", connector webhooks, Button triggers, and HTTP triggers. You do NOT need to ask the user to manually trigger the flow or wait for the next scheduled run.

>

The only case where resubmit is not available is a brand-new flow that has never run — it has no prior run to replay.

# Resubmit the failed run — works for ANY trigger type
resubmit = mcp("resubmit_live_flow_run",
    environmentName=ENV, flowName=FLOW_ID, runName=RUN_ID)
print(resubmit)   # {"resubmitted": true, "triggerName": "..."}

# Wait ~30 s then check
import time; time.sleep(30)
new_runs = mcp("get_live_flow_runs", environmentName=ENV, flowName=FLOW_ID, top=3)
print(new_runs[0]["status"])   # Succeeded = done

When to use resubmit vs trigger

Testing HTTP-Triggered Flows with custom payloads

For flows with a Request (HTTP) trigger, use trigger_live_flow when you need to send a different payload than the original run:

# First inspect what the trigger expects — read directly from the flow definition
defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
triggers = defn["properties"]["definition"]["triggers"]
manual = next(iter(triggers.values()))   # usually the only trigger on HTTP flows
request_schema = manual.get("inputs", {}).get("schema")
print("Expected body schema:", request_schema)

# Response schemas live on Response action(s) in the actions block
for name, act in defn["properties"]["definition"]["actions"].items():
    if act.get("type") == "Response":
        print(f"Response {name}:", act.get("inputs", {}).get("schema"))

# Trigger with a test payload
result = mcp("trigger_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,
    body={"name": "Test User", "value": 42})
print(f"Status: {result['responseStatus']}, Body: {result.get('responseBody')}")

trigger_live_flow handles AAD-authenticated triggers automatically. Only works for flows with a Request (HTTP) trigger type.

Quick-Reference Diagnostic Decision Tree

Rule: never diagnose from error codes alone. get_live_flow_run_error identifies the failing action. get_live_flow_run_action_outputs reveals the actual cause. Always call both.

Reference Files

• common-errors.md — Error codes, likely causes, and fixes
• debug-workflow.md — Full decision tree for complex failures

Related Skills

• flowstudio-power-automate-mcp — Foundation skill: connection setup, MCP helper, tool discovery
• flowstudio-power-automate-build — Build and deploy new flows