Programmatic Power Automate flow management via FlowStudio MCP server.
Works with
List, read, and monitor cloud flows directly from the Power Automate API without UI or manual steps
Inspect run history, per-action error details, and trigger outputs; resubmit failed runs or cancel active executions
Update flow definitions, manage connections, and retrieve HTTP-triggered flow callback URLs
Requires FlowStudio MCP subscription with JWT token authentication; Python or Node.js helper functions pr
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionflowstudio-power-automate-mcpExecute the skills CLI command in your project's root directory to begin installation:
Fetches flowstudio-power-automate-mcp from github/awesome-copilot and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate flowstudio-power-automate-mcp. Access via /flowstudio-power-automate-mcp in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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This skill lets AI agents read, monitor, and operate Microsoft Power Automate cloud flows programmatically through a FlowStudio MCP server — no browser, no UI, no manual steps.
Requires: A FlowStudio MCP subscription (or compatible Power Automate MCP server). You will need:
- MCP endpoint:
https://mcp.flowstudio.app/mcp(same for all subscribers)- API key / JWT token (
x-api-keyheader — NOT Bearer)- Power Platform environment name (e.g.
Default-<tenant-guid>)
| Priority | Source | Covers |
|---|---|---|
| 1 | Real API response | Always trust what the server actually returns |
| 2 | tools/list |
Tool names, parameter names, types, required flags |
| 3 | SKILL docs & reference files | Response shapes, behavioral notes, workflow recipes |
Start every new session with
tools/list. It returns the authoritative, up-to-date schema for every tool — parameter names, types, and required flags. The SKILL docs cover whattools/listcannot tell you: response shapes, non-obvious behaviors, and end-to-end workflow patterns.If any documentation disagrees with
tools/listor a real API response, the API wins.
All examples in this skill and the companion build / debug skills use Python
with urllib.request (stdlib — no pip install needed). Node.js is an
equally valid choice: fetch is built-in from Node 18+, JSON handling is
native, and the async/await model maps cleanly onto the request-response pattern
of MCP tool calls — making it a natural fit for teams already working in a
JavaScript/TypeScript stack.
| Language | Verdict | Notes |
|---|---|---|
| Python | ✅ Recommended | Clean JSON handling, no escaping issues, all skill examples use it |
| Node.js (≥ 18) | ✅ Recommended | Native fetch + JSON.stringify/JSON.parse; async/await fits MCP call patterns well; no extra packages needed |
| PowerShell | ⚠️ Avoid for flow operations | ConvertTo-Json -Depth silently truncates nested definitions; quoting and escaping break complex payloads. Acceptable for a quick tools/list discovery call but not for building or updating flows. |
| cURL / Bash | ⚠️ Possible but fragile | Shell-escaping nested JSON is error-prone; no native JSON parser |
TL;DR — use the Core MCP Helper (Python or Node.js) below. Both handle JSON-RPC framing, auth, and response parsing in a single reusable function.
FlowStudio MCP has two access tiers. FlowStudio for Teams subscribers get both the fast Azure-table store (cached snapshot data + governance metadata) and full live Power Automate API access. MCP-only subscribers get the live tools — more than enough to build, debug, and operate flows.
| Tool | What it does |
|---|---|
list_live_flows |
List flows in an environment directly from the PA API (always current) |
list_live_environments |
List all Power Platform environments visible to the service account |
list_live_connections |
List all connections in an environment from the PA API |
get_live_flow |
Fetch the complete flow definition (triggers, actions, parameters) |
get_live_flow_http_schema |
Inspect the JSON body schema and response schemas of an HTTP-triggered flow |
get_live_flow_trigger_url |
Get the current signed callback URL for an HTTP-triggered flow |
trigger_live_flow |
POST to an HTTP-triggered flow's callback URL (AAD auth handled automatically) |
update_live_flow |
Create a new flow or patch an existing definition in one call |
add_live_flow_to_solution |
Migrate a non-solution flow into a solution |
get_live_flow_runs |
List recent run history with status, start/end times, and errors |
get_live_flow_run_error |
Get structured error details (per-action) for a failed run |
get_live_flow_run_action_outputs |
Inspect inputs/outputs of any action (or every foreach iteration) in a run |
resubmit_live_flow_run |
Re-run a failed or cancelled run using its original trigger payload |
cancel_live_flow_run |
Cancel a currently running flow execution |
These tools read from (and write to) the FlowStudio Azure table — a monitored snapshot of your tenant's flows enriched with governance metadata and run statistics.
| Tool | What it does |
|---|---|
list_store_flows |
Search flows from the cache with governance flags, run failure rates, and owner metadata |
get_store_flow |
Get full cached details for a single flow including run stats and governance fields |
get_store_flow_trigger_url |
Get the trigger URL from the cache (instant, no PA API call) |
get_store_flow_runs |
Cached run history for the last N days with duration and remediation hints |
get_store_flow_errors |
Cached failed-only runs with failed action names and remediation hints |
get_store_flow_summary |
Aggregated stats: success rate, failure count, avg/max duration |
set_store_flow_state |
Start or stop a flow via the PA API and sync the result back to the store |
update_store_flow |
Update governance metadata (description, tags, monitor flag, notification rules, business impact) |
list_store_environments |
List all environments from the cache |
list_store_makers |
List all makers (citizen developers) from the cache |
get_store_maker |
Get a maker's flow/app counts and account status |
list_store_power_apps |
List all Power Apps canvas apps from the cache |
list_store_connections |
List all Power Platform connections from the cache |
| Task | Tool | Notes |
|---|---|---|
| List flows | list_live_flows |
Always current — calls PA API directly |
| Read a definition | get_live_flow |
Always fetched live — not cached |
| Debug a failure | get_live_flow_runs → get_live_flow_run_error |
Use live run data |
⚠️
list_live_flowsreturns a wrapper object with aflowsarray — access viaresult["flows"].
Store tools (
list_store_flows,get_store_flow, etc.) are available to FlowStudio for Teams subscribers and provide cached governance metadata. Use live tools when in doubt — they work for all subscription tiers.
Always start by calling tools/list to confirm the server is reachable and see
exactly which tool names are available (names may vary by server version):
import json, urllib.request
TOKEN = "<YOUR_JWT_TOKEN>"
MCP = "https://mcp.flowstudio.app/mcp"
def mcp_raw(method, params=None, cid=1):
payload = {"jsonrpc": "2.0", "method": method, "id": cid}
if params:
payload["params"] = params
req = urllib.request.Request(MCP, data=json.dumps(payload).encode(),
headers={"x-api-key": TOKEN, "Content-Type": "application/json",
"User-Agent": "FlowStudio-MCP/1.0"})
try:
resp = urllib.request.urlopen(req, timeout=30)
except urllib.error.HTTPError as e:
raise RuntimeError(f"MCP HTTP {e.code} — check token and endpoint") from e
return json.loads(resp.read())
raw = mcp_raw("tools/list")
if "error" in raw:
print("ERROR:", raw["error"]); raise SystemExit(1)
for t in raw["result"]["tools"]:
print(t["name"], "—", t["description"][:60])
Use this helper throughout all subsequent operations:
import json, urllib.request
TOKEN = "<YOUR_JWT_TOKEN>"
MCP = "https://mcp.flowstudio.app/mcp"
def mcp(tool, args, cid=1):
payload = {"jsonrpc": "2.0", "method": "tools/call", "id": cid,
"params": {"name": tool, "arguments": args}}
req = urllib.request.Request(MCP, data=json.dumps(payload).encode(),
headers={"x-api-key": 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'])}")
text = raw["result"]["content"][0]["text"]
return json.loads(text)
Common auth errors:
- HTTP 401/403 → token is missing, expired, or malformed. Get a fresh JWT from mcp.flowstudio.app.
- HTTP 400 → malformed JSON-RPC payload. Check
Content-Type: application/jsonand body structure.MCP error: {"code": -32602, ...}→ wrong or missing tool arguments.
Equivalent helper for Node.js 18+ (built-in fetch — no packages required):
✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.6★★★★★61 reviews
CCharlotte Mensah★★★★★Dec 24, 2024I recommend flowstudio-power-automate-mcp for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
SSofia Lopez★★★★★Dec 16, 2024flowstudio-power-automate-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
PPratham Ware★★★★★Dec 8, 2024Registry listing for flowstudio-power-automate-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.
MMin Ghosh★★★★★Dec 8, 2024Useful defaults in flowstudio-power-automate-mcp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
CCharlotte Okafor★★★★★Dec 8, 2024flowstudio-power-automate-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
SSakshi Patil★★★★★Nov 27, 2024flowstudio-power-automate-mcp reduced setup friction for our internal harness; good balance of opinion and flexibility.
KKabir Martinez★★★★★Nov 27, 2024flowstudio-power-automate-mcp is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
AAdvait Martin★★★★★Nov 27, 2024We added flowstudio-power-automate-mcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
AAlexander Tandon★★★★★Nov 27, 2024Solid pick for teams standardizing on skills: flowstudio-power-automate-mcp is focused, and the summary matches what you get after install.
KKabir Jain★★★★★Nov 15, 2024Keeps context tight: flowstudio-power-automate-mcp is the kind of skill you can hand to a new teammate without a long onboarding doc.
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