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---
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name: flowstudio-power-automate-mcp
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description: >-
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Foundation skill for Power Automate via FlowStudio MCP — auth setup, the
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reusable MCP helper (Python + Node.js), tool discovery via `list_skills` /
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`tool_search`, and oversized-response handling. Load this skill first when
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connecting an agent to Power Automate. For specialized workflows, load
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`power-automate-build`, `power-automate-debug`, `power-automate-monitoring`
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(Pro+), or `power-automate-governance` (Pro+) — each contains the workflow
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narrative, this skill provides the plumbing they all rely on. Requires a
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FlowStudio MCP subscription or compatible server — see https://mcp.flowstudio.app
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metadata:
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openclaw:
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requires:
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env:
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- FLOWSTUDIO_MCP_TOKEN
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primaryEnv: FLOWSTUDIO_MCP_TOKEN
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homepage: https://mcp.flowstudio.app
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---
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# Power Automate via FlowStudio MCP — Foundation
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This skill is the **plumbing layer**. It gives an AI agent a reliable way to
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talk to a FlowStudio MCP server, discover what tools are available, and handle
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the responses cleanly. The actual workflow narratives live in four specialized
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skills that all build on this one.
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> **Real debugging examples**: [Expression error in child flow](https://github.com/ninihen1/power-automate-mcp-skills/blob/main/examples/fix-expression-error.md) |
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> [Data entry, not a flow bug](https://github.com/ninihen1/power-automate-mcp-skills/blob/main/examples/data-not-flow.md) |
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> [Null value crashes child flow](https://github.com/ninihen1/power-automate-mcp-skills/blob/main/examples/null-child-flow.md)
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> **Requires:** A [FlowStudio](https://mcp.flowstudio.app) MCP subscription (or
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> compatible Power Automate MCP server). You will need:
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> - MCP endpoint: `https://mcp.flowstudio.app/mcp` (same for all subscribers)
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> - API key / JWT token (`x-api-key` header — NOT Bearer)
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> - Power Platform environment name (e.g. `Default-<tenant-guid>`)
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---
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## Which Skill to Use When
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Skills are organized by **use-case intent**, not by which tools they call.
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Multiple skills reuse the same underlying tools — pick by what the user is
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trying to accomplish.
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| The user wants to… | Load this skill |
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|---|---|
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| Make or change a flow (build new, modify existing, fix a bug, deploy) | **`power-automate-build`** |
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| Diagnose why a flow failed (root cause analysis on a failing run) | **`power-automate-debug`** |
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| See tenant-wide flow health, failure rates, asset inventory | **`power-automate-monitoring`** *(Pro+)* |
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| Tag, audit, classify, score, or offboard flows | **`power-automate-governance`** *(Pro+)* |
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| Just connect, set up auth, write the helper, parse responses | this skill (foundation) |
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**Same tools, different lenses.** `power-automate-build` and `power-automate-debug`
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both call `update_live_flow`, `get_live_flow`, and the run-error tools — they
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differ in *direction* (forward vs backward) and *intent* (compose vs diagnose).
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`power-automate-monitoring` and `power-automate-governance` both call the Store
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tools — they differ in *audience* (ops vs compliance) and *outcome* (read
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health vs write metadata). Don't try to memorize "which tools belong to which
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skill"; pick the skill by what the user is doing.
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---
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## Source of Truth
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| Priority | Source | Covers |
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|----------|--------|--------|
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| 1 | **Real API response** | Always trust what the server actually returns |
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| 2 | **`tool_search` / `list_skills`** | Authoritative tool schemas, parameter names, types, required flags |
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| 3 | **SKILL docs & reference files** | Workflow narrative, response shapes, non-obvious behaviors |
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If documentation disagrees with a real API response, the API wins. Tool schemas
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in this skill (or any other) may lag the server — call `tool_search` to confirm
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the current shape before invoking a tool you haven't used recently.
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---
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## How Agents Discover Tools
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The FlowStudio MCP server (v1.1.5+) exposes two **non-billable** meta-tools that
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let an agent load only the tools relevant to the current task. Use these in
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preference to `tools/list` (which loads all 30+ schemas at once) or guessing
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tool names.
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| Meta-tool | When to call |
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|---|---|
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| `list_skills` | Cold start — see the available bundles (`build-flow`, `debug-flow`, `monitor-flow`, `discover`, `governance`) and pick one |
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| `tool_search` with `query: "skill:<name>"` | Load the full schema set for one bundle (e.g. `skill:debug-flow`) |
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| `tool_search` with `query: "select:tool1,tool2"` | Load specific tools by name (e.g. when chaining across bundles) |
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| `tool_search` with `query: "<keywords>"` | Free-text search when the user request is ambiguous (e.g. `"cancel run"`) |
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The server's `tool_search` bundles are intentionally **narrower than this
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skill family** — they're starter packs of the most-likely-needed tools per
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intent. A workflow skill (e.g. `power-automate-debug`) may pull a bundle and
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then call `tool_search` again for additional tools as the workflow progresses.
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```python
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# Cold start — pick a bundle by intent
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skills = mcp("list_skills", {})
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# [{"name": "debug-flow", "description": "Investigate why a flow is failing...",
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# "tools": ["get_live_flow_runs", "get_live_flow_run_error", ...]}, ...]
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# Load schemas for the bundle
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debug_tools = mcp("tool_search", {"query": "skill:debug-flow"})
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```
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---
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## Recommended Language: Python or Node.js
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All examples in this skill family use **Python with `urllib.request`**
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(stdlib — no `pip install` needed). **Node.js** is an equally valid choice:
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`fetch` is built-in from Node 18+, JSON handling is native, and async/await
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maps cleanly onto the request-response pattern of MCP tool calls — making it
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a natural fit for teams already working in a JavaScript/TypeScript stack.
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| Language | Verdict | Notes |
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|---|---|---|
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| **Python** | Recommended | Clean JSON handling, no escaping issues, all skill examples use it |
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| **Node.js (≥ 18)** | Recommended | Native `fetch` + `JSON.stringify`/`JSON.parse`; no extra packages |
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| PowerShell | Avoid for flow operations | `ConvertTo-Json -Depth` silently truncates nested definitions; quoting and escaping break complex payloads. Acceptable for a quick connectivity smoke-test but not for building or updating flows. |
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| cURL / Bash | Possible but fragile | Shell-escaping nested JSON is error-prone; no native JSON parser |
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> **TL;DR — use the Core MCP Helper (Python or Node.js) below.** Both handle
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> JSON-RPC framing, auth, and response parsing in a single reusable function.
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---
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## Core MCP Helper (Python)
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Use this helper throughout all subsequent operations:
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```python
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import json, urllib.request
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TOKEN = "<YOUR_JWT_TOKEN>"
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MCP = "https://mcp.flowstudio.app/mcp"
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def mcp(tool, args, cid=1):
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payload = {"jsonrpc": "2.0", "method": "tools/call", "id": cid,
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"params": {"name": tool, "arguments": args}}
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req = urllib.request.Request(MCP, data=json.dumps(payload).encode(),
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headers={"x-api-key": TOKEN, "Content-Type": "application/json",
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"User-Agent": "FlowStudio-MCP/1.0"})
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try:
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resp = urllib.request.urlopen(req, timeout=120)
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except urllib.error.HTTPError as e:
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body = e.read().decode("utf-8", errors="replace")
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raise RuntimeError(f"MCP HTTP {e.code}: {body[:200]}") from e
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raw = json.loads(resp.read())
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if "error" in raw:
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raise RuntimeError(f"MCP error: {json.dumps(raw['error'])}")
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text = raw["result"]["content"][0]["text"]
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return json.loads(text)
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```
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> **Common auth errors:**
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> - HTTP 401/403 → token is missing, expired, or malformed. Get a fresh JWT from [mcp.flowstudio.app](https://mcp.flowstudio.app).
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> - HTTP 400 → malformed JSON-RPC payload. Check `Content-Type: application/json` and body structure.
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> - `MCP error: {"code": -32602, ...}` → wrong or missing tool arguments. Call `tool_search` with `select:<toolname>` to confirm the schema.
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---
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## Core MCP Helper (Node.js)
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Equivalent helper for Node.js 18+ (built-in `fetch` — no packages required):
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```js
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const TOKEN = "<YOUR_JWT_TOKEN>";
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const MCP = "https://mcp.flowstudio.app/mcp";
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async function mcp(tool, args, cid = 1) {
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const payload = {
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jsonrpc: "2.0",
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method: "tools/call",
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id: cid,
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params: { name: tool, arguments: args },
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};
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const res = await fetch(MCP, {
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method: "POST",
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headers: {
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"x-api-key": TOKEN,
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"Content-Type": "application/json",
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"User-Agent": "FlowStudio-MCP/1.0",
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},
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body: JSON.stringify(payload),
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});
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if (!res.ok) {
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const body = await res.text();
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throw new Error(`MCP HTTP ${res.status}: ${body.slice(0, 200)}`);
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}
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const raw = await res.json();
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if (raw.error) throw new Error(`MCP error: ${JSON.stringify(raw.error)}`);
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return JSON.parse(raw.result.content[0].text);
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}
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```
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> Requires Node.js 18+. For older Node, replace `fetch` with `https.request`
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> from the stdlib or install `node-fetch`.
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---
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## Verify the Connection
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A 3-line smoke test that confirms the token, endpoint, and helper all work:
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```python
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skills = mcp("list_skills", {})
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print(f"Connected — {len(skills)} skill bundles available:",
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[s["name"] for s in skills])
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```
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Expected output:
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```text
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Connected — 5 skill bundles available: ['build-flow', 'debug-flow', 'monitor-flow', 'discover', 'governance']
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```
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If this fails, see the **Common auth errors** note above. If it succeeds, hand
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off to the workflow skill matching the user's intent.
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---
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## Handling Oversized Responses
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Some MCP tool responses are large enough to overflow the agent's context window:
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| Tool | Typical size | Cause |
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| `describe_live_connector` | 100-600 KB | Full Swagger spec for a connector |
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| `get_live_flow_run_action_outputs` (no `actionName`) | 50 KB – several MB | All actions × all foreach iterations |
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| `get_live_flow` (large flows) | 50-500 KB | Deeply nested branches |
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| `list_live_flows` (large tenants) | 50-200 KB | Hundreds of flow records |
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### When the harness spills to a file
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Agent harnesses (Claude Code, VS Code Copilot, etc.) save oversized responses
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to a temp file (e.g. `tool-results/mcp-flowstudio-describe_live_connector-NNNN.txt`)
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and return the path instead of the inline JSON. The file is **double-wrapped** —
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the outer MCP envelope plus the inner JSON-escaped payload:
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```text
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[{"type":"text","text":"<JSON-escaped payload>"}]
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```
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Two parses to reach a usable object:
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```python
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import json
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with open(path) as f:
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raw = json.loads(f.read())
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payload = json.loads(raw[0]["text"])
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```
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```powershell
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$payload = ((Get-Content $path -Raw | ConvertFrom-Json)[0].text) | ConvertFrom-Json
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```
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### Rules of thumb
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1. **Extract, don't echo.** Pull the specific field(s) you need (one `operationId`, one action's outputs) and discard the rest before reasoning about it.
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2. **Always pass `actionName` to `get_live_flow_run_action_outputs`.** Omitting it fetches every action × every iteration — fine for offline debug scripts, dangerous for an agent that ingests the whole response.
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3. **Reuse the spill file within a session.** Refetching the same connector swagger costs 30+ seconds and produces another spill — cache the path.
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4. **Don't grep the spill file for JSON keys directly.** Strings are JSON-escaped inside the file (`\"OperationId\":`), so a plain grep for `"OperationId":` will not match. Parse first, then filter.
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5. **Summarize tool output to the user.** Echo `name + state + trigger` for flow lists and `actionName + status + code` for run errors — not raw JSON, unless asked.
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```python
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# Good — drill into one operation in a connector swagger
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conn = mcp("describe_live_connector", {"environmentName": ENV, "connectorName": "shared_sharepointonline"})
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op = conn["properties"]["swagger"]["paths"]["/datasets/{dataset}/tables/{table}/items"]["get"]
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print(op["operationId"], "—", op.get("summary"))
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# Bad — keeping the whole 500 KB swagger in context
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print(json.dumps(conn, indent=2)) # don't do this
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```
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---
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## Auth & Connection Notes
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| Field | Value |
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| Auth header | `x-api-key: <JWT>` — **not** `Authorization: Bearer` |
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| Token format | Plain JWT — do not strip, alter, or prefix it |
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| Timeout | Use ≥ 120 s for `get_live_flow_run_action_outputs` (large outputs) |
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| Environment name | `Default-<tenant-guid>` (find it via `list_live_environments` or `list_live_flows` response) |
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---
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## Reference Files
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- [MCP-BOOTSTRAP.md](references/MCP-BOOTSTRAP.md) — endpoint, auth, request/response format (read this first)
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- [tool-reference.md](references/tool-reference.md) — response shapes and behavioral notes (parameters are in `tool_search`)
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- [action-types.md](references/action-types.md) — Power Automate action type patterns
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- [connection-references.md](references/connection-references.md) — connector reference guide
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