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 `flows

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](https://github.com/ninihen1/power automate mcp skills/blob/main/examples/fix expression error.md) [Data entry, not a flow bug](https://github.com/ninihen1/power automate mcp skills/blob/main/examples/data not flow.md) [Null value crashes child flow](https://github.com/ninihen1/power automate mcp skills/blob/main/examples/null child flow.md) Requires: A [FlowStudio](https://mcp.flowstudio.app) 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](https://learn.flowstudio.app/chatgpt power automate) 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. The user wants to… Load this skill Make or change a flow (build new, modify existing, fix a bug, deploy) flowstudio power automate build Diagnose why a flow failed (root cause analysis on a failing run) flowstudio power automate debug See tenant wide flow health, failure rates, asset inventory flowstudio power automate monitoring (Pro+) Tag, audit, classify, score, or offboard flows flowstudio power automate governance (Pro+) Just connect, set up auth, write the helper, parse responses this skill (foundation) 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 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. 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. flowstudio power automate debug ) may pull a bundle and then call tool search again for additional tools as the workflow progresses. 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 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. Core MCP Helper (Python) Use this helper throughout all subsequent operations: Common auth errors: HTTP 401/403 → token is missing, expired, or malformed. Get a fresh JWT from [mcp.flowstudio.app](https://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) Equivalent helper for Node.js 18+ (built in fetch — no packages required): Requires Node.js 18+. For older Node, replace fetch with https.request from the stdlib or install node fetch . Verify the Connection A 3 line smoke test that confirms the token, endpoint, and helper all work: Expected output: If this fails, see the Common auth errors note above. If it succeeds, hand off to the workflow skill matching the user's intent. 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: Two parses to reach a usable object: 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. 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) Reference Files [MCP BOOTSTRAP.md](references/MCP BOOTSTRAP.md) — endpoint, auth, request/response format (read this first) [tool reference.md](references/tool reference.md) — response shapes and behavioral notes (parameters are in tool search ) [action types.md](references/action types.md) — Power Automate action type patterns [connection references.md](references/connection references.md) — connector reference guide

flowstudio-power-automate-mcp