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

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 diagnostic 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 — 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)

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)

get_live_flow_run_error

get_live_flow_run_action_outputs

ActionFailed

NotSpecified

InternalServerError

InvalidTemplate

BadRequest

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

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 get ALL actions in a single call. This returns every action's inputs/outputs — useful when you're not sure which upstream action produced the bad data. But use 120s+ timeout as the response can be very large.

Step 7 — Pinpoint the Root Cause

Expression Errors (e.g.

split

on null)

If the error mentions

InvalidTemplate

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']

fieldName

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

NotSpecified

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

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

resubmit_live_flow_run

resubmit_live_flow_run

resubmit_live_flow_run

trigger_live_flow

trigger_live_flow

Testing HTTP-Triggered Flows with custom payloads

For flows with a

Request

trigger_live_flow

# First inspect what the trigger expects
schema = mcp("get_live_flow_http_schema",
    environmentName=ENV, flowName=FLOW_ID)
print("Expected body schema:", schema.get("requestSchema"))
print("Response schemas:", schema.get("responseSchemas"))

# 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

get_live_flow_run_error

get_live_flow_run_action_outputs

outputs

ActionFailed

NotSpecified

get_live_flow_run_action_outputs

outputs.body

get_live_flow_run_action_outputs

outputs.statusCode

outputs.body

get_live_flow_run_action_outputs

get_live_flow

properties.state

get_live_flow_run_action_outputs

get_live_flow_runs

status

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

  — Core connection setup and operation reference

• flowstudio-power-automate-build

  — Build and deploy new flows