Power Automate Debugging with FlowStudio MCP

A step-by-step diagnostic process for investigating failing Power Automate

cloud flows 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 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 = "" 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 = ""

e.g. Default-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

FlowStudio for Teams: Fast-Path Diagnosis (Skip Steps 2–4) If you have a FlowStudio for Teams subscription, get_store_flow_errors returns per-run failure data including action names and remediation hints in a single call — no need to walk through live API steps.

Quick failure summary

summary

mcp ( "get_store_flow_summary" , environmentName = ENV , flowName = FLOW_ID )

{"totalRuns": 100, "failRuns": 10, "failRate": 0.1,

"averageDurationSeconds": 29.4, "maxDurationSeconds": 158.9,

"firstFailRunRemediation": ""}

print ( f"Fail rate: { summary [ 'failRate' ] : .0% } over { summary [ 'totalRuns' ] } runs" )

Per-run error details (requires active monitoring to be configured)

errors

mcp ( "get_store_flow_errors" , environmentName = ENV , flowName = FLOW_ID ) if errors : for r in errors [ : 3 ] : print ( r [ "startTime" ] , "|" , r . get ( "failedActions" ) , "|" , r . get ( "remediationHint" ) )

If errors confirms the failing action → jump to Step 6 (apply fix)

else :

Store doesn't have run-level detail for this flow — use live tools (Steps 2–5)

pass For the full governance record (description, complexity, tier, connector list): record = mcp ( "get_store_flow" , environmentName = ENV , flowName = FLOW_ID )

{"displayName": "My Flow", "state": "Started",

"runPeriodTotal": 100, "runPeriodFailRate": 0.1, "runPeriodFails": 10,

"runPeriodDurationAverage": 29410.8, ← milliseconds

"runError": "{\"code\": \"EACCES\", ...}", ← JSON string, parse it

"description": "...", "tier": "Premium", "complexity": "{...}"}

if record . get ( "runError" ) : last_err = json . loads ( record [ "runError" ] ) print ( "Last run error:" , last_err ) Step 1 — Locate the Flow result = mcp ( "list_live_flows" , environmentName = ENV )

Returns a wrapper object:

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 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 — 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 5 — Inspect Action Outputs (Walk Back from Failure) For each action leading up to the failure, inspect its runtime output: for action_name in [ "Compose_WeekEnd" , "HTTP_Get_Data" , "Parse_JSON" ] : result = mcp ( "get_live_flow_run_action_outputs" , environmentName = ENV , flowName = FLOW_ID , runName = RUN_ID , actionName = action_name )

Returns an array — single-element when actionName is provided

out

result [ 0 ] if result else { } print ( action_name , out . get ( "status" ) ) print ( json . dumps ( out . get ( "outputs" , { } ) , indent = 2 ) [ : 500 ] ) ⚠️ Output payloads from array-processing actions can be very large. Always slice (e.g. [:500] ) before printing. Step 6 — Pinpoint the Root Cause Expression Errors (e.g. split on null) If the error mentions InvalidTemplate or a function name: Find the action in the definition Check what upstream action/expression it reads 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" )

Returns a single-element array; index [0] to get the action object

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. Check the trigger output shape with: mcp ( "get_live_flow_run_action_outputs" , . . . , actionName = "" ) 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. Step 7 — 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 8 — Verify the Fix

Resubmit the failed run

resubmit

mcp ( "resubmit_live_flow_run" , environmentName = ENV , flowName = FLOW_ID , runName = RUN_ID ) print ( resubmit )

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

Testing HTTP-Triggered Flows For flows with a Request (HTTP) trigger, use trigger_live_flow instead of resubmit_live_flow_run to test with custom payloads:

First inspect what the trigger expects

schema

mcp ( "get_live_flow_http_schema" , environmentName = ENV , flowName = FLOW_ID ) print ( "Expected body schema:" , schema . get ( "triggerSchema" ) ) 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 [ 'status' ] } , Body: { result . get ( 'body' ) } " ) trigger_live_flow handles AAD-authenticated triggers automatically. Only works for flows with a Request (HTTP) trigger type. Quick-Reference Diagnostic Decision Tree Symptom First Tool to Call What to Look For Flow shows as Failed get_live_flow_run_error failedActions[-1]["actionName"] = root cause Expression crash get_live_flow_run_action_outputs on prior action null / wrong-type fields in output body Flow never starts get_live_flow check properties.state = "Started" Action returns wrong data get_live_flow_run_action_outputs actual output body vs expected Fix applied but still fails get_live_flow_runs after resubmit new run status field Reference Files common-errors.md — Error codes, likely causes, and fixes debug-workflow.md — Full decision tree for complex failures