Make MCP MCP | Api Key Make MCP connector for AI agents

CALL ANY TOOL

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

make_scenarios_list

List scenarios

List Make scenarios with active status and team filters.

Parameters

Name

Type

Required

Description

is_active

boolean

Optional

Filter by active status

team_id

integer

Optional

Team ID filter

limit

integer

Optional

Max scenarios

make_scenario_get

Get scenario

make_scenario_run

Run scenario

make_executions_list

List executions

make_execution_logs

Get execution logs

Build your Agent

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

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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 });

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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 }],
});

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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

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List all active scenarios.

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Get details for [scenario name].

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Which scenarios failed in the last 24 hours?

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List scenarios in [team].

Runs & logs

Run [scenario name].

List last 10 executions of [scenario].

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Get logs for execution [id].

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Which executions took over 60 seconds?

Reporting

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Total executions this month.

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Error rate by scenario.

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Which scenarios run most frequently?

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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.

1

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

2

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

3

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

4

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.

Make MCP

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