How do I find my Metricool Access Token?

Log in to Metricool, go to Account Settings > API, and you can generate or copy your Access Token there.

Can I see metrics for multiple platforms?

Yes, Metricool supports Instagram, Facebook, Twitter, LinkedIn, and more. Use the specific tool for each platform.

Is my social media data secure?

Absolutely. Your token is encrypted at rest and injected securely at runtime.

Metricool MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Metricool
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Metricool through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Metricool "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Metricool?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Metricool MCP Server

Connect your Metricool account to any AI agent and take full control of your social media performance and planning through natural conversation.

Pydantic AI validates every Metricool tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Social Analytics — Retrieve detailed metrics for Instagram, Facebook, Twitter, and LinkedIn profiles in real-time
Unified Summary — Access high-level cross-channel performance reports to understand your total digital reach
Content Planning — List and inspect your social media planner to stay ahead of upcoming scheduled posts
Ads Performance — Monitor spend and conversion data for social advertising platforms directly from your agent
Profile Management — Enumerate all connected brands and social accounts linked to your workspace

The Metricool MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Metricool to Pydantic AI via MCP

Follow these steps to integrate the Metricool MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Metricool with type-safe schemas

Why Use Pydantic AI with the Metricool MCP Server

Pydantic AI provides unique advantages when paired with Metricool through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Metricool integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Metricool connection logic from agent behavior for testable, maintainable code

Metricool + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Metricool MCP Server delivers measurable value.

Type-safe data pipelines: query Metricool with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Metricool tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Metricool and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Metricool responses and write comprehensive agent tests

Metricool MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Metricool to Pydantic AI via MCP:

get_ads_performance

Get performance for ads

get_facebook_analytics

Get Facebook analytics

get_instagram_analytics

Get Instagram analytics

get_linkedin_analytics

Get LinkedIn analytics

get_profile_details

Get details for a specific profile

get_social_planner

Get scheduled posts planner

get_twitter_analytics

Get Twitter analytics

get_unified_summary

Get unified cross-channel summary

list_metricool_profiles

List all connected social profiles

list_published_posts

List recently published posts

Example Prompts for Metricool in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Metricool immediately.

"Show my Instagram analytics for the last 30 days."

"What posts are scheduled in my planner?"

"Show a summary of my performance across all channels."

Troubleshooting Metricool MCP Server with Pydantic AI

Common issues when connecting Metricool to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Metricool + Pydantic AI FAQ

Common questions about integrating Metricool MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Metricool MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.