- name
- flowstudio-power-automate-debug
- description
- Debug failing Power Automate cloud flows using the FlowStudio MCP server. The Graph API only shows top-level status codes. This skill gives your agent action-level inputs and outputs to find the actual root cause. 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
# 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](https://github.com/ninihen1/power-automate-mcp-skills/blob/main/examples/fix-expression-error.md) |
> [Data entry, not a flow bug](https://github.com/ninihen1/power-automate-mcp-skills/blob/main/examples/data-not-flow.md) |
> [Null value crashes child flow](https://github.com/ninihen1/power-automate-mcp-skills/blob/main/examples/null-child-flow.md)
**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
```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
```
---
## Step 1 — Locate the Flow
```python
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
```python
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.
```python
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.**
```python
# 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)
| Error code from `get_live_flow_run_error` | What `get_live_flow_run_action_outputs` reveals |
|---|---|
| `ActionFailed` | Which nested action actually failed and its HTTP response |
| `NotSpecified` | The HTTP status code + response body with the real error |
| `InternalServerError` | The server's error message, stack trace, or API error JSON |
| `InvalidTemplate` | The exact expression that failed and the null/wrong-type value |
| `BadRequest` | The request body that was sent and why the server rejected it |
### 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:
```python
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
```python
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:
```python
# 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
```python
# 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:
```python
result = mcp("get_live_flow_run_action_outputs", ..., actionName="<trigger-action-name>")
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