Debug failing Power Automate cloud flows using the FlowStudio MCP server. Load this skill when asked to: debug a flow, investigate a failed run, why is this flow failing, inspect action outputs, find the root cause of a flow error, fix a broken Power Automate flow, diagnose a timeout, trace a DynamicOperationRequestFailure, check connector auth errors, read error details from a run, or troubleshoot expression failures. Requires a FlowStudio MCP subscription — see https://mcp.flowstudio.app
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
Always call
tools/listfirst 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 — thingstools/listcannot tell you. If this document disagrees withtools/listor a real API response, the API wins.
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
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": "<hint or null>"}
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)