Metricool MCP

Live

OAUTH 2.1

SOCIAL SCHEDULING

Marketing

Metricool MCP gives agents authenticated access to social scheduling and analytics: schedule posts, read engagement metrics, and find the best time to post per network.

  • Per-user credentials: each call uses the actual user's token, never a shared bot.
  • Encrypted per-tenant vault: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: pre-call scope check, 90-day SIEM-exportable audit chain.
Metricool MCP
agent · Acme Q3
Run
What's the best time to post on Instagram this week, and schedule tomorrow's post then.
S
metricoolmcp_getbesttimetopostbynetwork
133ms
Metricool agent
Best window is Thu 11:00 local. Scheduled tomorrow's post for that slot.
Sources: 1 brand, Instagram
metricoolmcp
1 brand
18:29
Message Claude...

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

CALL ANY TOOL
Schedule posts and read analytics end to end, scoped to each user's own Metricool brand accounts.
metricoolmcp_getbrandsettings
Getbrandsettings
Get the list of brands from your Metricool account. Only Instagram, Facebook, Twitch, YouTube, Twitter, and Bluesky support competitors.
Parameters
Name
Type
Required
Description
No parameters required
metricoolmcp_getscheduledposts
Getscheduledposts
metricoolmcp_createscheduledpost
Createscheduledpost
metricoolmcp_updatescheduledpost
Updatescheduledpost
metricoolmcp_getanalyticsdatabymetrics
Getanalyticsdatabymetrics
metricoolmcp_getbesttimetopostbynetwork
Getbesttimetopostbynetwork
metricoolmcp_sendscheduledpostforreview
Sendscheduledpostforreview
metricoolmcp_createscheduledpostforreview
Createscheduledpostforreview
metricoolmcp_getanalyticsavailablemetrics
Getanalyticsavailablemetrics
Build your Agent
Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.
Python · LlamaIndex
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.
GTM and RevOps Teams
Outbound prospecting agent
Searches Apollo for prospects matching your ICP, scores and ranks them, drafts personalized outreach in Gmail, and logs every send to Google Sheets. Mail goes out as the rep, not from a shared inbox.
GTM and RevOps Teams
CRM AI agent
Reads the Granola transcript after every call, extracts next steps and updates the HubSpot record, drafts the follow-up in Gmail, and confirms in Slack, all on the rep's own delegated OAuth.
GTM and RevOps Teams
Deal intelligence agent
Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.
GTM and RevOps Teams
Competitive intelligence briefing agent
Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.
Test other agents
See the same per-user auth pattern across other marketing and analytics connectors.
SALES
Outbound prospecting agent
Search Apollo for ICP matches, rank them, draft personalised Gmail outreach, and log every send to Google Sheets.
GTM
CRM AI agent
Turn each Granola call transcript into a HubSpot record update, a drafted Gmail follow-up, and a Slack recap.
SALES
Deal intelligence agent
Score Gong call risk with an LLM, cross-reference the Attio record, and DM each owner their at-risk deals in Slack.
GTM
Competitive intelligence briefing agent
Scan Gong calls for competitor mentions, match each one to its Notion battlecard, and DM every affected rep a single Slack digest.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
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.
Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""metricoolmcp"": {
""url"": ""https://mcp.scalekit.com/metricoolmcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.metricoolmcp]
url = ""https://mcp.scalekit.com/metricoolmcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""metricoolmcp"": {
""url"": ""https://mcp.scalekit.com/metricoolmcp"",
""type"": ""http""
}
}
}