power-automate-mcp

name

power-automate-mcp

description

Foundation skill for Power Automate via FlowStudio MCP — auth setup, the reusable MCP helper (Python + Node.js), tool discovery via `list_skills` / `tool_search`, and oversized-response handling. Load this skill first when connecting an agent to Power Automate. For specialized workflows, load `power-automate-build`, `power-automate-debug`, `power-automate-monitoring` (Pro+), or `power-automate-governance` (Pro+) — each contains the workflow narrative, this skill provides the plumbing they all rely on. Requires a FlowStudio MCP subscription or compatible server — see https://mcp.flowstudio.app

Power Automate via FlowStudio MCP — Foundation

This skill is the plumbing layer. It gives an AI agent a reliable way to talk to a FlowStudio MCP server, discover what tools are available, and handle the responses cleanly. The actual workflow narratives live in four specialized skills that all build on this one.

Real debugging examples: Expression error in child flow | Data entry, not a flow bug | Null value crashes child flow

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-key header — NOT Bearer)
• Power Platform environment name (e.g. Default-<tenant-guid>)

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Which Skill to Use When

Skills are organized by use-case intent, not by which tools they call. Multiple skills reuse the same underlying tools — pick by what the user is trying to accomplish.

The user wants to…	Load this skill
Make or change a flow (build new, modify existing, fix a bug, deploy)	power-automate-build
Diagnose why a flow failed (root cause analysis on a failing run)	power-automate-debug
See tenant-wide flow health, failure rates, asset inventory	power-automate-monitoring (Pro+)
Tag, audit, classify, score, or offboard flows	power-automate-governance (Pro+)
Just connect, set up auth, write the helper, parse responses	this skill (foundation)

Same tools, different lenses. power-automate-build and power-automate-debug both call update_live_flow, get_live_flow, and the run-error tools — they differ in direction (forward vs backward) and intent (compose vs diagnose). power-automate-monitoring and power-automate-governance both call the Store tools — they differ in audience (ops vs compliance) and outcome (read health vs write metadata). Don't try to memorize "which tools belong to which skill"; pick the skill by what the user is doing.

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Source of Truth

Priority	Source	Covers
1	Real API response	Always trust what the server actually returns
2	tool_search / list_skills	Authoritative tool schemas, parameter names, types, required flags
3	SKILL docs & reference files	Workflow narrative, response shapes, non-obvious behaviors

If documentation disagrees with a real API response, the API wins. Tool schemas in this skill (or any other) may lag the server — call tool_search to confirm the current shape before invoking a tool you haven't used recently.

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How Agents Discover Tools

The FlowStudio MCP server (v1.1.5+) exposes two non-billable meta-tools that let an agent load only the tools relevant to the current task. Use these in preference to tools/list (which loads all 30+ schemas at once) or guessing tool names.

Meta-tool	When to call
list_skills	Cold start — see the available bundles (build-flow, create-flow, debug-flow, monitor-flow, discover, governance) and pick one
tool_search with query: "skill:<name>"	Load the full schema set for one bundle (e.g. skill:debug-flow)
tool_search with query: "select:tool1,tool2"	Load specific tools by name (e.g. when chaining across bundles)
tool_search with query: "<keywords>"	Free-text search when the user request is ambiguous (e.g. "cancel run")

The server's tool_search bundles are intentionally narrower than this skill family — they're starter packs of the most-likely-needed tools per intent. A workflow skill (e.g. power-automate-debug) may pull a bundle and then call tool_search again for additional tools as the workflow progresses.

# Cold start — pick a bundle by intent
skills = mcp("list_skills", {})
# [{"name": "debug-flow", "description": "Investigate why a flow is failing...",
#   "tools": ["get_live_flow_runs", "get_live_flow_run_error", ...]}, ...]

# Load schemas for the bundle
debug_tools = mcp("tool_search", {"query": "skill:debug-flow"})

Current common bundles:

Bundle	Use when
create-flow	Creating a brand-new flow; includes environment/connection discovery, connector description, dynamic options, and update_live_flow
build-flow	Reading or modifying an existing flow definition
debug-flow	Investigating failed runs and action-level inputs/outputs
monitor-flow	Starting/stopping, triggering, cancelling, or resubmitting runs
discover	Enumerating environments, flows, and connections
governance	Pro+ cached-store tagging, maker audit, and metadata updates

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Recommended Language: Python or Node.js

All examples in this skill family 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 async/await 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; no extra packages
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.
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.

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Core MCP Helper (Python)

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/json and body structure.
• MCP error: {"code": -32602, ...} → wrong or missing tool arguments. Call tool_search with select:<toolname> to confirm the schema.

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Core MCP Helper (Node.js)

Equivalent helper for Node.js 18+ (built-in fetch — no packages required):

const TOKEN = "<YOUR_JWT_TOKEN>";
const MCP   = "https://mcp.flowstudio.app/mcp";

async function mcp(tool, args, cid = 1) {
  const payload = {
    jsonrpc: "2.0",
    method: "tools/call",
    id: cid,
    params: { name: tool, arguments: args },
  };
  const res = await fetch(MCP, {
    method: "POST",
    headers: {
      "x-api-key": TOKEN,
      "Content-Type": "application/json",
      "User-Agent": "FlowStudio-MCP/1.0",
    },
    body: JSON.stringify(payload),
  });
  if (!res.ok) {
    const body = await res.text();
    throw new Error(`MCP HTTP ${res.status}: ${body.slice(0, 200)}`);
  }
  const raw = await res.json();
  if (raw.error) throw new Error(`MCP error: ${JSON.stringify(raw.error)}`);
  return JSON.parse(raw.result.content[0].text);
}

Requires Node.js 18+. For older Node, replace fetch with https.request from the stdlib or install node-fetch.

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Verify the Connection

A 3-line smoke test that confirms the token, endpoint, and helper all work:

skills = mcp("list_skills", {})
print(f"Connected — {len(skills)} skill bundles available:",
      [s["name"] for s in skills])

Expected output:

Connected — 6 skill bundles available: ['build-flow', 'create-flow', 'debug-flow', 'monitor-flow', 'discover', 'governance']

If this fails, see the Common auth errors note above. If it succeeds, hand off to the workflow skill matching the user's intent.

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Handling Oversized Responses

Some MCP tool responses are large enough to overflow the agent's context window:

Tool	Typical size	Cause
describe_live_connector	100-600 KB	Full Swagger spec for a connector
get_live_dynamic_properties	50-500 KB	Dynamic connector field schemas such as SharePoint list columns
get_live_flow_run_action_outputs (no actionName)	50 KB – several MB	Top-level action outputs; with an action in a foreach, every repetition can be returned
get_live_flow (large flows)	50-500 KB	Deeply nested branches
list_live_flows (large tenants)	50-200 KB	Hundreds of flow records

When the harness spills to a file

Agent harnesses (Claude Code, VS Code Copilot, etc.) save oversized responses to a temp file (e.g. tool-results/mcp-flowstudio-describe_live_connector-NNNN.txt) and return the path instead of the inline JSON. The file is double-wrapped — the outer MCP envelope plus the inner JSON-escaped payload:

[{"type":"text","text":"<JSON-escaped payload>"}]

Two parses to reach a usable object:

import json
with open(path) as f:
    raw = json.loads(f.read())
payload = json.loads(raw[0]["text"])

$payload = ((Get-Content $path -Raw | ConvertFrom-Json)[0].text) | ConvertFrom-Json

Rules of thumb

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.
2. Always pass actionName to get_live_flow_run_action_outputs. Omitting it fetches all top-level actions. For actions inside a foreach, passing actionName without iterationIndex can return every repetition of that action.
3. Reuse the spill file within a session. Refetching the same connector swagger costs 30+ seconds and produces another spill — cache the path.
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.
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.

# Good — drill into one operation in a connector swagger
conn = mcp("describe_live_connector", {"environmentName": ENV, "connectorName": "shared_sharepointonline"})
op = conn["properties"]["swagger"]["paths"]["/datasets/{dataset}/tables/{table}/items"]["get"]
print(op["operationId"], "—", op.get("summary"))

# Bad — keeping the whole 500 KB swagger in context
print(json.dumps(conn, indent=2))   # don't do this

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Auth & Connection Notes

Field	Value
Auth header	x-api-key: <JWT> — not Authorization: Bearer
Token format	Plain JWT — do not strip, alter, or prefix it
Timeout	Use ≥ 120 s for get_live_flow_run_action_outputs (large outputs)
Environment name	Default-<tenant-guid> (find it via list_live_environments or list_live_flows response)

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Reference Files

• MCP-BOOTSTRAP.md — endpoint, auth, request/response format (read this first)
• tool-reference.md — response shapes and behavioral notes (parameters are in tool_search)
• action-types.md — Power Automate action type patterns
• connection-references.md — connector reference guide