Supermetrics MCP for AI agents | OAuth 2.0 connector

CALL ANY TOOL

List connected ad sources, query cross-channel metrics, run saved reports, and discover available metrics.

sm_data_sources_list

List data sources

List all data sources connected in the Supermetrics account.

Parameters

Name

Type

Required

Description

—

No parameters required

sm_query_create

Run data query

sm_reports_list

List saved reports

sm_metrics_get

Get available metrics

sm_accounts_list

List accounts

Build your Agent

Drop the toolkit in, point it at the user, and your marketing analytics agent can use Supermetrics 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: ["supermetricsmcp"], toolNames: ["sm_data_sources_list", "sm_query_create", "sm_reports_list"] },
  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: ["supermetricsmcp"], toolNames: ["sm_data_sources_list", "sm_query_create", "sm_reports_list"] },
  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: ["supermetricsmcp"], toolNames: ["sm_data_sources_list", "sm_query_create", "sm_reports_list"] },
  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/supermetricsmcp",
    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 Supermetrics MCP.

Cross-channel queries

Copy the prompt

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Compare Google vs Meta spend and ROAS this month.

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Query LinkedIn Ads: impressions, clicks, spend last 30 days.

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Top 5 campaigns by conversions across all channels.

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Cross-channel daily spend trend last 90 days.

Sources & accounts

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List all connected data sources.

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List all ad accounts for Google Ads.

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What metrics are available for TikTok Ads?

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List all saved Supermetrics reports.

Reporting

Copy the prompt

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MOM spend comparison for all channels.

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ROAS by campaign for the last quarter.

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Which campaigns have CPA above target?

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Platform with best ROAS this month.

SEE HOW AUTH WORKS

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

1

Authorize

Your user connects

Supermetrics 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

Supermetrics 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

Supermetrics 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

Supermetrics 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 marketing analytics agents and MCP connectors. Working code, live demos, fork what fits.

GTM

HubSpot to Slack updates agent

Watch HubSpot deal stage changes and post structured updates to the right Slack channel. Reps stop checking the CRM all day.

HubSpot

Slack

View agent

GTM

Salesforce customer insights agent

Surface Salesforce account activity, NPS signals, and renewal flags into Slack threads for the account team.

Salesforce

Slack

View agent

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.

Supermetrics 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 Supermetrics 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 Supermetrics MCP oauth 2.0 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 Supermetrics MCP?

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

What happens when a user revokes Supermetrics 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 access ad accounts connected by other team members?

Only accounts connected under the authorizing user's Supermetrics OAuth. Accounts added by other team members require those users to grant access explicitly. Supermetrics team sharing settings apply.

Supermetrics MCP

Tools your marketing analytics agent reaches for on Supermetrics MCP, scoped per user.