Metricool MCP connector for AI agents

CALL ANY TOOL

Schedule posts and read analytics end to end, scoped to each user's own Metricool brand accounts.

metricoolmcp_getbrandsettings

Get brand settings

Get the list of brands from your Metricool account.

Parameters

Name

Type

Required

Description

—

No parameters required

metricoolmcp_createscheduledpost

Create scheduled post

metricoolmcp_getscheduledposts

Get scheduled posts

metricoolmcp_getbesttimetopostbynetwork

Get best time to post

metricoolmcp_getanalyticsdatabymetrics

Get analytics data

Build your Agent

Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.

import { ScalekitClient } from "@scalekit-sdk/node";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

// Metricool tools scoped to this user
const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["metricoolmcp"], toolNames: [
    "metricoolmcp_getbrandsettings",
    "metricoolmcp_createscheduledpost",
    "metricoolmcp_getscheduledposts"] },
  pageSize: 100,
});

const agent = createReactAgent({ llm, tools });
await agent.invoke({ messages: [{ role: "user", content: "Find the best time to post on Instagram this week and schedule tomorrow's post" }] });

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

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["metricoolmcp"] }, pageSize: 100,
});

const res = await openai.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "Find the best time to post on Instagram this week and schedule tomorrow's post" }],
  tools,
});

// Execute the tool call with the user's vaulted metricoolmcp credential
await sk.tools.executeTool(res.choices[0].message.tool_calls[0], "user_123");

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

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["metricoolmcp"] }, pageSize: 100,
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-5",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Find the best time to post on Instagram this week and schedule tomorrow's post" }],
  tools,
});

// Tool call runs with the user's vaulted metricoolmcp credential
await sk.tools.executeTool(msg.content, "user_123");

import { Agent } from "@google/adk/agents";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["metricoolmcp"] }, pageSize: 100,
});

const agent = new Agent({
  name: "metricoolmcp_agent",
  model: "gemini-2.5-pro",
  instruction: "Metricool tools scoped to this user",
  tools,
});

await agent.run("Find the best time to post on Instagram this week and schedule tomorrow's post");

Try these prompts

Copy any prompt into your agent. Each maps directly to a Metricool MCP tool. Click to copy, paste into your agent, done.

Schedule content

Copy the prompt

Copied

Schedule tomorrow's post for 11am on Instagram and LinkedIn.

Copy the prompt

Copied

Get the list of scheduled posts for this brand next week.

Copy the prompt

Copied

Update scheduled post 4021 with new caption text.

Best time and brand setup

Copy the prompt

Copied

What's the best time to post on TikTok this week?

Copy the prompt

Copied

List the brands connected to this Metricool account.

Copy the prompt

Copied

Get the best posting window for LinkedIn in America/New_York.

Analytics

Copy the prompt

Copied

Get available analytics metrics for Instagram.

Copy the prompt

Copied

Pull engagement data for the last 30 days on Twitter.

Copy the prompt

Copied

Compare reach across networks for this month.

SEE HOW AUTH WORKS

Your users connect once. Their Metricool MCP credentials stay vaulted, every call is checked, and every action is logged.

1

Authorize

Your user connects

Metricool 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

Metricool 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

Metricool 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

Metricool 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

See the same per-user auth pattern across other marketing and analytics connectors.

SALES

Outbound prospecting agent

Build targeted prospect lists with Apollo, enrich with firmographic data, and draft personalised outreach. Runs on a schedule.

Apollo

View agent

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

Why Scalekit

Secure your agent's access. Connectors ship in minutes

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

01.

Shared tokens break per-user analytics

A shared Metricool MCP token looks fine in a demo. In production every scheduled post looks like one service account, and you cannot tell which user triggered it. Scalekit resolves the credential of the actual user who triggered the agent, never a shared bot.

// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Metricool MCP today. Ten connectors 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 Metricool MCP as the user or through a shared key?

As the user. Scalekit resolves the credential of the person who triggered the agent at request time, so every Metricool MCP action in your audit trail is attributed to a real user, not a shared service account.

Where is the Metricool MCP token stored?

In an AES-256 encrypted vault with per-tenant namespacing. Tokens are resolved at request time, never enter LLM context, refresh automatically, and can be revoked from one dashboard.

Can I limit what the agent does in Metricool MCP?

Yes. Filter by tool name in listScopedTools to expose only what you want. Scalekit also enforces scope checks before every API call.

What happens when a user revokes access?

The credential is invalidated at the next tool call. The call fails closed, other users' connections are unaffected, and the revocation is logged in the audit chain.

Can the agent post to a brand's social accounts without a human approving first?

Only if you scope createscheduledpost into the tool set you expose. Keep the agent to read-only analytics and best-time tools, or require a human step before scheduling goes live.

Metricool MCP

Tools your marketing agent reaches for on Metricool, scoped per user.