Make MCP | API Key Make MCP connector for AI agents

Tools your automation agent reaches for on Make MCP, scoped per user.

CALL ANY TOOL

List and run scenarios, retrieve execution history, and debug failures with detailed logs.

makemcp_app_documentation_get

Get app documentation

Retrieves markdown documentation for the specific Make App. Use when configuring Make Apps and Modules and when you need to learn more about the available capabilities.

Parameters

Name

Type

Required

Description

appName

string

Required

Name of the App to get the documentation for.

makemcp_app-module_get

Get app-module

makemcp_app-modules_list

List app-modules

makemcp_apps_recommend

Apps Recommend

makemcp_connection-metadata_get

Get connection-metadata

makemcp_connections_get

Get connections

makemcp_connections_list

List connections

makemcp_credential-requests_create

Create credential-requests

makemcp_credential-requests_create-by-credentials

Credential-Requests Create-By-Credentials

makemcp_credential-requests_credential-decline

Credential-Requests Credential-Decline

makemcp_credential-requests_credential-delete

Credential-Requests Credential-Delete

makemcp_credential-requests_delete

Delete credential-requests

makemcp_credential-requests_extend-connection

Credential-Requests Extend-Connection

makemcp_credential-requests_get

Get credential-requests

makemcp_credential-requests_list

List credential-requests

makemcp_credential-requests_list-app-modules-with-creden

Credential-Requests List-App-Modules-With-Creden

makemcp_custom_apps_connections_configure

Custom Apps Connections Configure

makemcp_custom_apps_connections_delete

Delete custom apps connections

makemcp_custom_apps_connections_fetch

Fetch custom apps connections

makemcp_custom_apps_create

Create custom apps

makemcp_custom_apps_delete

Delete custom apps

makemcp_custom_apps_fetch

Fetch custom apps

makemcp_custom_apps_functions_create

Create custom apps functions

makemcp_custom_apps_functions_delete

Delete custom apps functions

makemcp_custom_apps_functions_fetch

Fetch custom apps functions

makemcp_custom_apps_functions_get_code

Custom Apps Functions Get Code

makemcp_custom_apps_functions_get_test

Custom Apps Functions Get Test

makemcp_custom_apps_functions_set_code

Custom Apps Functions Set Code

makemcp_custom_apps_functions_set_test

Custom Apps Functions Set Test

makemcp_custom_apps_get_example

Custom Apps Get Example

For more tools, view docs.

Build your Agent

Drop the toolkit in, point it at the user, and your automation agent can use Make MCP from the first run.

import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["makemcp"], toolNames: ["make_scenarios_list", "make_scenario_get", "make_scenario_run"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["makemcp"], toolNames: ["make_scenarios_list", "make_scenario_get", "make_scenario_run"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["makemcp"], toolNames: ["make_scenarios_list", "make_scenario_get", "make_scenario_run"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/makemcp",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your agent to start using Make MCP.

Scenarios & status

Copy the prompt

Copied

List all active scenarios.

Copy the prompt

Copied

Get details for [scenario name].

Copy the prompt

Copied

Which scenarios failed in the last 24 hours?

Copy the prompt

Copied

List scenarios in [team].

Runs & logs

Run [scenario name].

List last 10 executions of [scenario].

Copy the prompt

Copied

Get logs for execution [id].

Copy the prompt

Copied

Which executions took over 60 seconds?

Reporting

Copy the prompt

Copied

Total executions this month.

Copy the prompt

Copied

Error rate by scenario.

Copy the prompt

Copied

Which scenarios run most frequently?

Copy the prompt

Copied

Scenarios with no executions in 7 days.

SEE HOW AUTH WORKS

Users authorize Make MCP once. Their credentials stay vaulted, every call is checked, and every action is logged.

Authorize

Your user connects

Make MCP

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

Store

Their

Make MCP

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

Resolve

When your agent calls a

Make MCP

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

Audit

Every

Make MCP

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

Same per-user auth pattern across other automation agents and MCP connectors. Working code, live demos, fork what fits.

ENGINEERING

Slack workflow agent (LangGraph)

LangGraph agent that drives multi-step Slack workflows: triggers, approvals, and follow-up actions per user identity.

Slack

View agent

ENGINEERING

DevOps assistant agent

Triage GitHub incidents, open Linear tickets, and notify the on-call channel in Slack with context already attached.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

01.

Shared tokens break per-user analytics

A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Make MCP today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

FAQs

Frequently Asked Questions

Does the agent access Make MCP as the user or as a shared key?

As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Make MCP api key stored?

In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in Make MCP?

Yes. Pass a tool name filter to listScopedTools so the automation agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Make MCP.

What happens when a user revokes Make MCP access?

The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Can the agent trigger scenarios in other Make organizations the user has access to?

One organization per API key. The key is scoped to the Make organization that generated it. Cross-organization scenario access requires a separate connected account and key per org.

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit