Power Automate MCP Server by Flow Studio MCP Server
io.github.ninihen1/flowstudio-mcp
Debug, build, and manage Power Automate flows with AI agents—see action inputs, outputs, and nested failures that Graph API hides.
What is the Power Automate MCP Server by Flow Studio MCP server?
The Power Automate MCP Server by Flow Studio gives AI agents deep visibility into Power Automate cloud flows, exposing action-level inputs, outputs, loop iterations, and error details that the standard Graph API cannot provide. It enables agents to debug failing flows, build new flow definitions, monitor flow health, and enforce governance policies across your tenant.
This MCP server bridges the gap between what humans see in the Power Automate portal and what AI agents can access via Graph API. Instead of only seeing top-level run status, agents gain full visibility into action inputs/outputs, child flow details, loop iteration data, and error context. It includes five integrated skills: core flow operations (list, read, run, cancel), debugging workflows, flow building and deployment, monitoring and health analytics, and governance/compliance auditing.
How to install Power Automate MCP Server by Flow Studio
Copy-paste configuration for popular MCP clients.
x-api-keyrequiredsecretFlow Studio MCP API key (get one at https://mcp.flowstudio.app)
Tools & capabilities
Tools this server exposes to the agent.
power-automate-mcp— Connect to and operate Power Automate cloud flows—list flows, read definitions, check runs, resubmit, and cancel runs.power-automate-debug— Step-by-step diagnostic process for investigating failing flows with action-level visibility.power-automate-build— Build, scaffold, and deploy Power Automate flow definitions from scratch.power-automate-monitoring— Flow health, failure rates, maker inventory, Power Apps, environment and connection counts (cached daily snapshot).power-automate-governance— Classify flows by impact, detect orphans, audit connectors, manage notifications, and compute archive scores.
Use cases
- Debug a failing flow by tracing through action inputs/outputs, child flows, and loop iterations to find the root cause without opening the portal.
- Build and deploy new Power Automate flows from natural language descriptions using AI agents.
- Monitor flow health and failure rates across your tenant to identify at-risk flows and maker activity patterns.
- Audit connectors and detect orphaned flows for governance and compliance without installing the CoE Starter Kit.
- Resubmit or cancel failed runs and update live flow definitions directly from your agent.
Power Automate MCP Server by Flow Studio MCP server FAQ
It's an MCP server that gives AI agents the same visibility into Power Automate flows that humans have in the portal—including action inputs, outputs, loop iterations, and nested error details—plus tools to debug, build, monitor, and govern flows.
No. It requires a FlowStudio MCP subscription. Monitoring and governance tools require FlowStudio for Teams or MCP Pro+. You can get a token at mcp.flowstudio.app.
Use the command: `claude mcp add --transport http flowstudio https://mcp.flowstudio.app/mcp --header "x-api-key: <YOUR_TOKEN>"` for Claude Code, or add the server to `.vscode/mcp.json` for Cursor/VS Code with the same URL and API key header.
You need a FlowStudio account and API token (passed as the `x-api-key` HTTP header). Sign in with your Microsoft account at mcp.flowstudio.app to generate a token.
It works with Copilot, Claude Code, ChatGPT, Codex, OpenClaw, Gemini CLI, Cursor, Goose, Amp, and OpenHands—any agent compatible with the MCP protocol.
Graph API only returns top-level run status (pass/fail). This MCP server exposes action-level inputs and outputs, error details, loop iteration data, child flow runs, and governance metadata that agents cannot access via Graph API alone.
README (reference)
Source of truth, from the repository.
FlowStudio MCP — Power Automate Skills for AI Agents
Give your AI agent the same visibility you have in the Power Automate portal — plus a bit more. The Graph API only returns top-level run status — agents can't see action inputs, loop iterations, or nested failures. FlowStudio MCP exposes all of it.

You can click through the portal and find the root cause. Your agent can't — unless it has MCP.


When you need this
- Your agent can see that a flow failed, but not why — Graph API only returns status codes
- You want your agent to see action-level inputs and outputs, like you can in the portal
- A loop has hundreds of iterations and some produced bad output — in the portal you'd click through each one, but the agent can scan all iteration inputs and outputs at once
- You want to check flow health, failure rates, and maker activity across your tenant without opening the admin center
- You need to classify flows, detect orphaned resources, or audit connectors at scale — without installing the CoE Starter Kit
- You're tired of being the middle-man between your agent and the portal
Graph API vs FlowStudio MCP
The core difference: Graph API gives your agent run status. MCP gives your agent the inputs and outputs of every action.
| What the agent sees | Graph API | FlowStudio MCP |
|---|---|---|
| Run passed or failed | Yes | Yes |
| Action inputs and outputs | No | Yes |
| Error details beyond status code | No | Yes |
| Child flow run details | No | Yes |
| Loop iteration data | No | Yes |
| Flow definition (read + write) | Limited | Full JSON |
| Resubmit / cancel runs | Limited | Yes |
| Cached flow health & failure rates | No | Yes |
| Maker / Power Apps / connection inventory | No | Yes |
| Governance metadata (tags, impact, owner) | No | Yes |
Skills
| Skill | Description |
|---|---|
power-automate-mcp | Connect to and operate Power Automate cloud flows — list flows, read definitions, check runs, resubmit, cancel |
power-automate-debug | Step-by-step diagnostic process for investigating failing flows |
power-automate-build | Build, scaffold, and deploy Power Automate flow definitions from scratch |
power-automate-monitoring | Flow health, failure rates, maker inventory, Power Apps, environment and connection counts |
power-automate-governance | Classify flows by impact, detect orphans, audit connectors, manage notifications, compute archive scores |
The first three skills use live Power Automate API calls. The monitoring and governance skills use the cached store — a daily snapshot with aggregated stats, remediation hints, and governance metadata. Requires a FlowStudio for Teams or MCP Pro+ subscription for store tools.
Each skill follows the Agent Skills specification and works with any compatible agent.
Supported agents
Copilot, Claude Code, ChatGPT, Codex, OpenClaw, Gemini CLI, Cursor, Goose, Amp, OpenHands
Guides
Full walkthroughs on learn.flowstudio.app:
- Getting started — install FlowStudio MCP for any agent
- Build flows with AI agents — a worked 3-step example
- Vibe code Power Automate — describe the flow, the agent builds it
- Power Automate in VS Code — work on flows from your editor
- Debug a failing flow — find the root cause from action-level data
- By agent: ChatGPT · Claude · GitHub Copilot · Copilot Studio · Codex
Quick Start
Connect from ChatGPT or claude.ai
Sign in with your Microsoft account instead of pasting a key. Add
https://mcp.flowstudio.app/mcp/oauth as a connector in ChatGPT (Developer mode
on) or at claude.ai, then follow the sign-in prompt. Walkthrough:
ChatGPT for Power Automate.
Install as Claude Code plugin
Available through the Claude plugin marketplace after approval. To test locally:
git clone https://github.com/ninihen1/power-automate-mcp-skills.git
claude --plugin-dir ./power-automate-mcp-skills
Then connect the MCP server:
claude mcp add --transport http flowstudio https://mcp.flowstudio.app/mcp \
--header "x-api-key: <YOUR_TOKEN>"
Get your token at mcp.flowstudio.app.
Install in Codex
Inside a Codex session, install skills directly:
$skill-installer install https://github.com/ninihen1/power-automate-mcp-skills/tree/main/skills/power-automate-mcp
$skill-installer install https://github.com/ninihen1/power-automate-mcp-skills/tree/main/skills/power-automate-debug
$skill-installer install https://github.com/ninihen1/power-automate-mcp-skills/tree/main/skills/power-automate-build
$skill-installer install https://github.com/ninihen1/power-automate-mcp-skills/tree/main/skills/power-automate-monitoring
$skill-installer install https://github.com/ninihen1/power-automate-mcp-skills/tree/main/skills/power-automate-governance
Then connect the MCP server in ~/.codex/config.toml:
[mcp_servers.flowstudio]
url = "https://mcp.flowstudio.app/mcp"
[mcp_servers.flowstudio.http_headers]
x-api-key = "<YOUR_TOKEN>"
Install via skills.sh
Search for flowstudio on skills.sh, or:
npx skills add github/awesome-copilot -s flowstudio-power-automate-mcp
npx skills add github/awesome-copilot -s flowstudio-power-automate-debug
npx skills add github/awesome-copilot -s flowstudio-power-automate-build
npx skills add github/awesome-copilot -s flowstudio-power-automate-monitoring
npx skills add github/awesome-copilot -s flowstudio-power-automate-governance
Install via ClawHub
npx clawhub@latest install power-automate-mcp
Install via Smithery
npx smithery skill add flowstudio/power-automate-mcp
Manual install
Copy the skill folder(s) into your project's .github/skills/ directory
(or wherever your agent discovers skills).
Connect the MCP server
Claude Code:
claude mcp add --transport http flowstudio https://mcp.flowstudio.app/mcp \
--header "x-api-key: <YOUR_TOKEN>"
Codex (~/.codex/config.toml):
[mcp_servers.flowstudio]
url = "https://mcp.flowstudio.app/mcp"
[mcp_servers.flowstudio.http_headers]
x-api-key = "<YOUR_TOKEN>"
Copilot / VS Code (.vscode/mcp.json):
{
"servers": {
"flowstudio": {
"type": "http",
"url": "https://mcp.flowstudio.app/mcp",
"headers": { "x-api-key": "<YOUR_TOKEN>" }
}
}
}
Get your token at mcp.flowstudio.app.
Real debugging examples
These are from real production investigations, not demos.
-
Expression error in child flow —
contains(string(...))crashed on a nested property. Agent traced through parent flow, into child, through loop iterations, and found the failing input. Portal showed "ExpressionEvaluationFailed" with no context. -
Data entry, not a flow bug — User reported two "bugs" back to back. Agent proved both were data entry errors (missing comma in email, single address in CC field). Flow was correct. Diagnosed in seconds.
-
Null value crashes child flow —
split(Name, ', ')crashed when 38% of records had null Names. Agent traced parent to child to loop to action, found the root cause, and deployed a fix viaupdate_live_flow.
Prerequisites
- A FlowStudio MCP subscription (all live tools)
- For store tools (monitoring, governance): FlowStudio for Teams or MCP Pro+
- MCP endpoint:
https://mcp.flowstudio.app/mcp - API key / JWT token (passed as
x-api-keyheader)
Repository structure
skills/
power-automate-mcp/ core connection & operation skill
power-automate-debug/ debug workflow skill
power-automate-build/ build & deploy skill
power-automate-monitoring/ flow health & tenant inventory skill
power-automate-governance/ compliance & governance skill
examples/ real debugging walkthroughs
README.md
LICENSE MIT
Available on GitHub
Works with Copilot, Claude, ChatGPT, and any MCP-compatible agent.
- awesome-copilot (merged)
- skills.sh (3K+ installs)
- Smithery (published)
- ClawHub (v1.1.0)
Contributing
Contributions welcome. Each skill folder must contain a SKILL.md with the
required frontmatter. See the existing skills for the format.
License
Keywords: Power Automate debugging, flow run history, expression evaluation failed, child flow failure, nested action errors, loop iteration output, agent automation MCP, Power Platform AI, flow definition deploy, resubmit failed run, flow monitoring, governance, CoE, orphan detection, connector audit, archive score, maker inventory
Related MCP servers

Version-controlled golden datasets and RAG evaluation, no API key needed.
MCP server for checking package versions across multiple package managers

Bible corpus MCP server: scripture, Greek/Hebrew interlinear data, cross-refs, semantic search.

io.github.nirajagarwal/stox-mcp
Percentile fingerprints, curated peers, theme clusters & market-stress context for US stocks.

3D AI Agent Avatar
Render 3D GLB avatars with Solana wallets, voice, and pump.fun token integration for AI agents.

three.ws 3D Agent Tools
Free + paid text-to-3D generation, rigging, and agent tools for AI-powered 3D model creation.
