flowstudio-power-automate-mcp

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Power Automate via FlowStudio MCP

通过FlowStudio MCP使用Power Automate

This skill lets AI agents read, monitor, and operate Microsoft Power Automate cloud flows programmatically through a FlowStudio MCP server — no browser, no UI, no manual steps.

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>
)

该技能可让AI Agent通过FlowStudio MCP服务器以编程方式读取、监控和操作Microsoft Power Automate云流，无需浏览器、无需操作UI、也无需手动步骤。

要求： 需拥有FlowStudio MCP订阅（或兼容的Power Automate MCP服务器）。你需要准备：
MCP端点：
https://mcp.flowstudio.app/mcp
（所有订阅用户通用）
API密钥 / JWT令牌（放在
x-api-key
请求头中，不是Bearer认证）
Power Platform环境名称（例如
Default-<租户GUID>
）

Source of Truth

可信信息来源

Priority	Source	Covers
1	Real API response	Always trust what the server actually returns
2	`tools/list`	Tool names, parameter names, types, required flags
3	SKILL docs & reference files	Response shapes, behavioral notes, workflow recipes

Start every new session with
tools/list
. It returns the authoritative, up-to-date schema for every tool — parameter names, types, and required flags. The SKILL docs cover what
tools/list
cannot tell you: response shapes, non-obvious behaviors, and end-to-end workflow patterns.
If any documentation disagrees with
tools/list
or a real API response, the API wins.

优先级	来源	覆盖范围
1	实际API响应	始终以服务器实际返回的结果为准
2	`tools/list`	工具名称、参数名称、参数类型、必填标识
3	技能文档与参考文件	响应结构、行为说明、工作流示例

每次新会话都请先调用
tools/list
。它会返回所有工具的权威、最新的schema定义，包括参数名称、类型和必填标识。技能文档会补充
tools/list
无法提供的信息：响应结构、非显性行为、端到端工作流模式。
如果任何文档内容与
tools/list
返回结果或实际API响应冲突，以API结果为准。

Recommended Language: Python or Node.js

推荐使用语言：Python或Node.js

All examples in this skill and the companion build / debug skills 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 the async/await model 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` ; async/await fits MCP call patterns well; no extra packages needed
PowerShell	⚠️ Avoid for flow operations	`ConvertTo-Json -Depth` silently truncates nested definitions; quoting and escaping break complex payloads. Acceptable for a quick `tools/list` discovery call 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.

本技能及配套的构建/调试技能的所有示例均使用Python的
urllib.request
（标准库，无需

pip install

）。Node.js也是完全合适的选择：Node 18+内置

fetch

，原生支持JSON处理，async/await模型与MCP工具调用的请求-响应模式匹配度很高，非常适合已经使用JavaScript/TypeScript技术栈的团队。

语言	推荐度	说明
Python	✅ 推荐	简洁的JSON处理，无转义问题，所有技能示例均使用该语言
Node.js (≥ 18)	✅ 推荐	原生支持 `fetch` + `JSON.stringify` / `JSON.parse` ；async/await非常适配MCP调用模式；无需额外安装依赖包
PowerShell	⚠️ 不建议用于流操作	`ConvertTo-Json -Depth` 会静默截断嵌套定义；引号和转义会导致复杂 payload 出错。仅可用于快速调用 `tools/list` 做探索，不适合构建或更新流
cURL / Bash	⚠️ 可实现但稳定性差	Shell转义嵌套JSON极易出错；无原生JSON解析器

总结：使用下方的核心MCP辅助函数（Python或Node.js版本）。 两者都在单个可复用函数中实现了JSON-RPC帧处理、鉴权和响应解析。

What You Can Do

支持的操作

FlowStudio MCP has two access tiers. FlowStudio for Teams subscribers get both the fast Azure-table store (cached snapshot data + governance metadata) and full live Power Automate API access. MCP-only subscribers get the live tools — more than enough to build, debug, and operate flows.

FlowStudio MCP有两个访问层级。FlowStudio for Teams订阅用户可同时使用快速Azure表存储（缓存快照数据+治理元数据）和完整的Power Automate实时API访问权限。仅MCP订阅用户可使用实时工具，完全满足构建、调试和操作流的需求。

Live Tools — Available to All MCP Subscribers

实时工具 — 所有MCP订阅用户可用

Tool	What it does
`list_live_flows`	List flows in an environment directly from the PA API (always current)
`list_live_environments`	List all Power Platform environments visible to the service account
`list_live_connections`	List all connections in an environment from the PA API
`get_live_flow`	Fetch the complete flow definition (triggers, actions, parameters)
`get_live_flow_http_schema`	Inspect the JSON body schema and response schemas of an HTTP-triggered flow
`get_live_flow_trigger_url`	Get the current signed callback URL for an HTTP-triggered flow
`trigger_live_flow`	POST to an HTTP-triggered flow's callback URL (AAD auth handled automatically)
`update_live_flow`	Create a new flow or patch an existing definition in one call
`add_live_flow_to_solution`	Migrate a non-solution flow into a solution
`get_live_flow_runs`	List recent run history with status, start/end times, and errors
`get_live_flow_run_error`	Get structured error details (per-action) for a failed run
`get_live_flow_run_action_outputs`	Inspect inputs/outputs of any action (or every foreach iteration) in a run
`resubmit_live_flow_run`	Re-run a failed or cancelled run using its original trigger payload
`cancel_live_flow_run`	Cancel a currently running flow execution

工具	功能
`list_live_flows`	直接调用PA API列出环境中的流（数据始终是最新的）
`list_live_environments`	列出服务账号可见的所有Power Platform环境
`list_live_connections`	调用PA API列出环境中的所有连接
`get_live_flow`	获取完整的流定义（触发器、操作、参数）
`get_live_flow_http_schema`	查看HTTP触发流的JSON请求体结构和响应结构
`get_live_flow_trigger_url`	获取HTTP触发流的当前已签名回调URL
`trigger_live_flow`	向HTTP触发流的回调URL发送POST请求（自动处理AAD鉴权）
`update_live_flow`	单次调用即可创建新流或更新现有流定义
`add_live_flow_to_solution`	将非解决方案流迁移到解决方案中
`get_live_flow_runs`	列出近期运行历史，包含状态、起止时间和错误信息
`get_live_flow_run_error`	获取失败运行的结构化错误详情（按操作拆分）
`get_live_flow_run_action_outputs`	查看运行中任意操作（或每个foreach迭代）的输入/输出
`resubmit_live_flow_run`	使用原始触发器payload重新运行失败或已取消的任务
`cancel_live_flow_run`	取消当前正在运行的流执行任务

Store Tools — FlowStudio for Teams Subscribers Only

存储工具 — 仅FlowStudio for Teams订阅用户可用

These tools read from (and write to) the FlowStudio Azure table — a monitored snapshot of your tenant's flows enriched with governance metadata and run statistics.

Tool	What it does
`list_store_flows`	Search flows from the cache with governance flags, run failure rates, and owner metadata
`get_store_flow`	Get full cached details for a single flow including run stats and governance fields
`get_store_flow_trigger_url`	Get the trigger URL from the cache (instant, no PA API call)
`get_store_flow_runs`	Cached run history for the last N days with duration and remediation hints
`get_store_flow_errors`	Cached failed-only runs with failed action names and remediation hints
`get_store_flow_summary`	Aggregated stats: success rate, failure count, avg/max duration
`set_store_flow_state`	Start or stop a flow via the PA API and sync the result back to the store
`update_store_flow`	Update governance metadata (description, tags, monitor flag, notification rules, business impact)
`list_store_environments`	List all environments from the cache
`list_store_makers`	List all makers (citizen developers) from the cache
`get_store_maker`	Get a maker's flow/app counts and account status
`list_store_power_apps`	List all Power Apps canvas apps from the cache
`list_store_connections`	List all Power Platform connections from the cache

这些工具读写FlowStudio Azure表，表中存储了租户流的监控快照，补充了治理元数据和运行统计信息。

工具	功能
`list_store_flows`	从缓存中搜索流，包含治理标识、运行失败率和所有者元数据
`get_store_flow`	获取单个流的完整缓存详情，包括运行统计和治理字段
`get_store_flow_trigger_url`	从缓存中获取触发器URL（即时返回，无需调用PA API）
`get_store_flow_runs`	最近N天的缓存运行历史，包含耗时和修复建议
`get_store_flow_errors`	仅缓存的失败运行记录，包含失败操作名称和修复建议
`get_store_flow_summary`	聚合统计数据：成功率、失败次数、平均/最大耗时
`set_store_flow_state`	通过PA API启动或停止流，并将结果同步回存储
`update_store_flow`	更新治理元数据（描述、标签、监控标识、通知规则、业务影响）
`list_store_environments`	从缓存中列出所有环境
`list_store_makers`	从缓存中列出所有制作者（公民开发者）
`get_store_maker`	获取制作者的流/应用数量和账号状态
`list_store_power_apps`	从缓存中列出所有Power Apps画布应用
`list_store_connections`	从缓存中列出所有Power Platform连接

Which Tool Tier to Call First

优先调用的工具层级

Task	Tool	Notes
List flows	`list_live_flows`	Always current — calls PA API directly
Read a definition	`get_live_flow`	Always fetched live — not cached
Debug a failure	`get_live_flow_runs` → `get_live_flow_run_error`	Use live run data

⚠️ list_live_flows
returns a wrapper object with a
flows
array — access via
result["flows"]
.

Store tools (
list_store_flows
,
get_store_flow
, etc.) are available to FlowStudio for Teams subscribers and provide cached governance metadata. Use live tools when in doubt — they work for all subscription tiers.

任务	工具	说明
列出流	`list_live_flows`	数据始终最新，直接调用PA API
读取流定义	`get_live_flow`	始终实时获取，无缓存
调试失败任务	`get_live_flow_runs` → `get_live_flow_run_error`	使用实时运行数据

⚠️ list_live_flows
返回的是封装对象，包含
flows
数组，通过
result["flows"]
访问。

存储工具（
list_store_flows
、
get_store_flow
等）仅对FlowStudio for Teams订阅用户开放，提供缓存的治理元数据。有疑问时请使用实时工具，所有订阅层级都可使用。

Step 0 — Discover Available Tools

步骤0 — 探索可用工具

Always start by calling

tools/list

to confirm the server is reachable and see exactly which tool names are available (names may vary by server version):

python

import json, urllib.request

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

def mcp_raw(method, params=None, cid=1):
    payload = {"jsonrpc": "2.0", "method": method, "id": cid}
    if params:
        payload["params"] = params
    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=30)
    except urllib.error.HTTPError as e:
        raise RuntimeError(f"MCP HTTP {e.code} — check token and endpoint") from e
    return json.loads(resp.read())

raw = mcp_raw("tools/list")
if "error" in raw:
    print("ERROR:", raw["error"]); raise SystemExit(1)
for t in raw["result"]["tools"]:
    print(t["name"], "—", t["description"][:60])

始终先调用

tools/list

确认服务器可访问，并查看确切的可用工具名称（名称可能因服务器版本不同有差异）：

python

import json, urllib.request

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

def mcp_raw(method, params=None, cid=1):
    payload = {"jsonrpc": "2.0", "method": method, "id": cid}
    if params:
        payload["params"] = params
    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=30)
    except urllib.error.HTTPError as e:
        raise RuntimeError(f"MCP HTTP {e.code} — check token and endpoint") from e
    return json.loads(resp.read())

raw = mcp_raw("tools/list")
if "error" in raw:
    print("ERROR:", raw["error"]); raise SystemExit(1)
for t in raw["result"]["tools"]:
    print(t["name"], "—", t["description"][:60])

Core MCP Helper (Python)

核心MCP辅助函数（Python）

Use this helper throughout all subsequent operations:

python

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.

后续所有操作都可使用该辅助函数：

python

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)

常见鉴权错误：
HTTP 401/403 → 令牌缺失、过期或格式错误。从mcp.flowstudio.app获取新的JWT即可。
HTTP 400 → JSON-RPC payload格式错误。检查
Content-Type: application/json
和请求体结构。
MCP error: {"code": -32602, ...}
→ 工具参数错误或缺失。

Core MCP Helper (Node.js)

核心MCP辅助函数（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
.

适用于Node.js 18+的等效辅助函数（内置

fetch

，无需安装依赖包）：

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);
}

要求Node.js 18+。旧版本Node可将
fetch
替换为标准库的
https.request
，或安装
node-fetch
包。

List Flows

列出流

python

ENV = "Default-<tenant-guid>"

result = mcp("list_live_flows", {"environmentName": ENV})

python

ENV = "Default-<tenant-guid>"

result = mcp("list_live_flows", {"environmentName": ENV})

Returns wrapper object:

返回封装对象：

{"mode": "owner", "flows": [{"id": "0757041a-...", "displayName": "My Flow",

"state": "Started", "triggerType": "Request", ...}], "totalCount": 42, "error": null}

for f in result["flows"]: FLOW_ID = f["id"] # plain UUID — use directly as flowName print(FLOW_ID, "|", f["displayName"], "|", f["state"])

---

for f in result["flows"]: FLOW_ID = f["id"] # 纯UUID，可直接作为flowName使用 print(FLOW_ID, "|", f["displayName"], "|", f["state"])

---

Read a Flow Definition

读取流定义

python

FLOW = "<flow-uuid>"

flow = mcp("get_live_flow", {"environmentName": ENV, "flowName": FLOW})

python

FLOW = "<flow-uuid>"

flow = mcp("get_live_flow", {"environmentName": ENV, "flowName": FLOW})

Display name and state

显示名称和状态

print(flow["properties"]["displayName"]) print(flow["properties"]["state"])

List all action names

列出所有操作名称

actions = flow["properties"]["definition"]["actions"] print("Actions:", list(actions.keys()))

Inspect one action's expression

查看单个操作的表达式

print(actions["Compose_Filter"]["inputs"])

---

print(actions["Compose_Filter"]["inputs"])

---

Check Run History

检查运行历史

python

undefined

python

undefined

Most recent runs (newest first)

最近的运行记录（按时间倒序）

runs = mcp("get_live_flow_runs", {"environmentName": ENV, "flowName": FLOW, "top": 5})

Returns direct array:

返回直接数组：

[{"name": "08584296068667933411438594643CU15",

"status": "Failed",

"startTime": "2026-02-25T06:13:38.6910688Z",

"endTime": "2026-02-25T06:15:24.1995008Z",

"triggerName": "manual",

"error": {"code": "ActionFailed", "message": "An action failed..."}},

{"name": "08584296028664130474944675379CU26",

"status": "Succeeded", "error": null, ...}]

for r in runs: print(r["name"], r["status"])

Get the name of the first failed run

获取第一个失败运行的名称

run_id = next((r["name"] for r in runs if r["status"] == "Failed"), None)

---

run_id = next((r["name"] for r in runs if r["status"] == "Failed"), None)

---

Inspect an Action's Output

查看操作输出

python

run_id = runs[0]["name"]

out = mcp("get_live_flow_run_action_outputs", {
    "environmentName": ENV,
    "flowName": FLOW,
    "runName": run_id,
    "actionName": "Get_Customer_Record"   # exact action name from the definition
})
print(json.dumps(out, indent=2))

python

run_id = runs[0]["name"]

out = mcp("get_live_flow_run_action_outputs", {
    "environmentName": ENV,
    "flowName": FLOW,
    "runName": run_id,
    "actionName": "Get_Customer_Record"   # 流定义中的精确操作名称
})
print(json.dumps(out, indent=2))

Get a Run's Error

获取运行错误信息

python

err = mcp("get_live_flow_run_error", {
    "environmentName": ENV,
    "flowName": FLOW,
    "runName": run_id
})

python

err = mcp("get_live_flow_run_error", {
    "environmentName": ENV,
    "flowName": FLOW,
    "runName": run_id
})

Returns:

{"runName": "08584296068...",

"failedActions": [

{"actionName": "HTTP_find_AD_User_by_Name", "status": "Failed",

"code": "NotSpecified", "startTime": "...", "endTime": "..."},

{"actionName": "Scope_prepare_workers", "status": "Failed",

"error": {"code": "ActionFailed", "message": "An action failed..."}}

],

"allActions": [

{"actionName": "Apply_to_each", "status": "Skipped"},

{"actionName": "Compose_WeekEnd", "status": "Succeeded"},

...

]}

The ROOT cause is usually the deepest entry in failedActions:

根本原因通常是failedActions中的最后一条记录：

root = err["failedActions"][-1] print(f"Root failure: {root['actionName']} → {root['code']}")

---

root = err["failedActions"][-1] print(f"Root failure: {root['actionName']} → {root['code']}")

---

Resubmit a Run

重新提交运行任务

python

result = mcp("resubmit_live_flow_run", {
    "environmentName": ENV,
    "flowName": FLOW,
    "runName": run_id
})
print(result)   # {"resubmitted": true, "triggerName": "..."}

python

result = mcp("resubmit_live_flow_run", {
    "environmentName": ENV,
    "flowName": FLOW,
    "runName": run_id
})
print(result)   # {"resubmitted": true, "triggerName": "..."}

Cancel a Running Run

取消正在运行的任务

python

mcp("cancel_live_flow_run", {
    "environmentName": ENV,
    "flowName": FLOW,
    "runName": run_id
})

⚠️ Do NOT cancel a run that shows
Running
because it is waiting for an adaptive card response. That status is normal — the flow is paused waiting for a human to respond in Teams. Cancelling it will discard the pending card.

python

mcp("cancel_live_flow_run", {
    "environmentName": ENV,
    "flowName": FLOW,
    "runName": run_id
})

⚠️ 如果运行状态显示
Running
是因为正在等待自适应卡片响应，请勿取消。该状态是正常的：流已暂停，等待用户在Teams中响应。取消会丢弃待处理的卡片。

Full Round-Trip Example — Debug and Fix a Failing Flow

完整端到端示例 — 调试并修复失败的流

python

undefined

python

undefined

── 1. Find the flow ─────────────────────────────────────────────────────

── 1. 查找目标流 ─────────────────────────────────────────────────────

result = mcp("list_live_flows", {"environmentName": ENV}) target = next(f for f in result["flows"] if "My Flow Name" in f["displayName"]) FLOW_ID = target["id"]

── 2. Get the most recent failed run ────────────────────────────────────

── 2. 获取最近的失败运行记录 ────────────────────────────────────

runs = mcp("get_live_flow_runs", {"environmentName": ENV, "flowName": FLOW_ID, "top": 5})

[{"name": "08584296068...", "status": "Failed", ...}, ...]

RUN_ID = next(r["name"] for r in runs if r["status"] == "Failed")

── 3. Get per-action failure breakdown ──────────────────────────────────

── 3. 获取按操作拆分的失败详情 ──────────────────────────────────

err = mcp("get_live_flow_run_error", {"environmentName": ENV, "flowName": FLOW_ID, "runName": RUN_ID})

{"failedActions": [{"actionName": "HTTP_find_AD_User_by_Name", "code": "NotSpecified",...}], ...}

root_action = err["failedActions"][-1]["actionName"] print(f"Root failure: {root_action}")

── 4. Read the definition and inspect the failing action's expression ───

── 4. 读取流定义，查看失败操作的表达式 ───

defn = mcp("get_live_flow", {"environmentName": ENV, "flowName": FLOW_ID}) acts = defn["properties"]["definition"]["actions"] print("Failing action inputs:", acts[root_action]["inputs"])

── 5. Inspect the prior action's output to find the null ────────────────

── 5. 查看前一个操作的输出，找到空值 ────────────────

out = mcp("get_live_flow_run_action_outputs", { "environmentName": ENV, "flowName": FLOW_ID, "runName": RUN_ID, "actionName": "Compose_Names" }) nulls = [x for x in out.get("body", []) if x.get("Name") is None] print(f"{len(nulls)} records with null Name")

── 6. Apply the fix ─────────────────────────────────────────────────────

── 6. 应用修复 ─────────────────────────────────────────────────────

acts[root_action]["inputs"]["parameters"]["searchName"] =
"@coalesce(item()?['Name'], '')"

conn_refs = defn["properties"]["connectionReferences"] result = mcp("update_live_flow", { "environmentName": ENV, "flowName": FLOW_ID, "definition": defn["properties"]["definition"], "connectionReferences": conn_refs }) assert result.get("error") is None, f"Deploy failed: {result['error']}"

acts[root_action]["inputs"]["parameters"]["searchName"] =
"@coalesce(item()?['Name'], '')"

⚠️ error key is always present — only fail if it is NOT None

⚠️ error字段始终存在，仅当它不为None时才代表部署失败

── 7. Resubmit and verify ───────────────────────────────────────────────

── 7. 重新提交并验证结果 ───────────────────────────────────────────────

mcp("resubmit_live_flow_run", {"environmentName": ENV, "flowName": FLOW_ID, "runName": RUN_ID})

import time; time.sleep(30) new_runs = mcp("get_live_flow_runs", {"environmentName": ENV, "flowName": FLOW_ID, "top": 1}) print(new_runs[0]["status"]) # Succeeded = done

---

mcp("resubmit_live_flow_run", {"environmentName": ENV, "flowName": FLOW_ID, "runName": RUN_ID})

import time; time.sleep(30) new_runs = mcp("get_live_flow_runs", {"environmentName": ENV, "flowName": FLOW_ID, "top": 1}) print(new_runs[0]["status"]) # 显示Succeeded即完成

---

Auth & Connection Notes

鉴权与连接说明

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)

字段	值
鉴权请求头	`x-api-key: <JWT>` — 不是 `Authorization: Bearer`
令牌格式	纯JWT，请勿裁剪、修改或添加前缀
超时设置	`get_live_flow_run_action_outputs` 请设置≥120秒的超时（输出可能很大）
环境名称	`Default-<租户GUID>` （可通过 `list_live_environments` 或 `list_live_flows` 响应获取）

Reference Files

参考文件

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

MCP-BOOTSTRAP.md — 端点、鉴权、请求/响应格式（请先阅读）
tool-reference.md — 响应结构和行为说明（参数定义见
```
tools/list
```
）
action-types.md — Power Automate操作类型模式
connection-references.md — 连接器参考指南

flowstudio-power-automate-mcp

Original

Translation

Power Automate via FlowStudio MCP

通过FlowStudio MCP使用Power Automate

Source of Truth

可信信息来源

Recommended Language: Python or Node.js

推荐使用语言：Python或Node.js

What You Can Do

支持的操作

Live Tools — Available to All MCP Subscribers

实时工具 — 所有MCP订阅用户可用

Store Tools — FlowStudio for Teams Subscribers Only

存储工具 — 仅FlowStudio for Teams订阅用户可用

Which Tool Tier to Call First

优先调用的工具层级

Step 0 — Discover Available Tools

步骤0 — 探索可用工具

Core MCP Helper (Python)

核心MCP辅助函数（Python）

Core MCP Helper (Node.js)

核心MCP辅助函数（Node.js）

List Flows

列出流

Returns wrapper object:

返回封装对象：

{"mode": "owner", "flows": [{"id": "0757041a-...", "displayName": "My Flow",

{"mode": "owner", "flows": [{"id": "0757041a-...", "displayName": "My Flow",

"state": "Started", "triggerType": "Request", ...}], "totalCount": 42, "error": null}

"state": "Started", "triggerType": "Request", ...}], "totalCount": 42, "error": null}

Read a Flow Definition

读取流定义

Display name and state

显示名称和状态

List all action names

列出所有操作名称

Inspect one action's expression

查看单个操作的表达式

Check Run History

检查运行历史

Most recent runs (newest first)

最近的运行记录（按时间倒序）

Returns direct array:

返回直接数组：

[{"name": "08584296068667933411438594643CU15",

[{"name": "08584296068667933411438594643CU15",

"status": "Failed",

"status": "Failed",

"startTime": "2026-02-25T06:13:38.6910688Z",

"startTime": "2026-02-25T06:13:38.6910688Z",

"endTime": "2026-02-25T06:15:24.1995008Z",

"endTime": "2026-02-25T06:15:24.1995008Z",

"triggerName": "manual",

"triggerName": "manual",

"error": {"code": "ActionFailed", "message": "An action failed..."}},

"error": {"code": "ActionFailed", "message": "An action failed..."}},

{"name": "08584296028664130474944675379CU26",

{"name": "08584296028664130474944675379CU26",

"status": "Succeeded", "error": null, ...}]

"status": "Succeeded", "error": null, ...}]

Get the name of the first failed run

获取第一个失败运行的名称

Inspect an Action's Output

查看操作输出

Get a Run's Error

获取运行错误信息

Returns:

返回：

{"runName": "08584296068...",

{"runName": "08584296068...",

"failedActions": [

"failedActions": [

{"actionName": "HTTP_find_AD_User_by_Name", "status": "Failed",

{"actionName": "HTTP_find_AD_User_by_Name", "status": "Failed",

"code": "NotSpecified", "startTime": "...", "endTime": "..."},

"code": "NotSpecified", "startTime": "...", "endTime": "..."},

{"actionName": "Scope_prepare_workers", "status": "Failed",

{"actionName": "Scope_prepare_workers", "status": "Failed",

"error": {"code": "ActionFailed", "message": "An action failed..."}}