Flowstudio Power Automate MCP

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) - In ChatGPT or claude.ai there is no key: add https://mcp.flowstudio.app/mcp/oauth as a connector and sign in with Microsoft — see the ChatGPT walkthrough - Power Platform environment name (e.g. Default-<tenant-guid>)

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.

Same tools, different lenses. flowstudio-power-automate-build and flowstudio-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). flowstudio-power-automate-monitoring and flowstudio-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.

Source of Truth

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.

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.

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. flowstudio-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:

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.

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.

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.

Core MCP Helper (Node.js)