Metricool MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Metricool through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"metricool": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Metricool, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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.
LangChain's ecosystem of 500+ components combines seamlessly with Metricool through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Metricool MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Metricool via MCP
Why Use LangChain with the Metricool MCP Server
LangChain provides unique advantages when paired with Metricool through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Metricool MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Metricool queries for multi-turn workflows
Metricool + LangChain Use Cases
Practical scenarios where LangChain combined with the Metricool MCP Server delivers measurable value.
RAG with live data: combine Metricool tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Metricool, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Metricool tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Metricool tool call, measure latency, and optimize your agent's performance
Metricool MCP Tools for LangChain (10)
These 10 tools become available when you connect Metricool to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Metricool to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersMetricool + LangChain FAQ
Common questions about integrating Metricool MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Metricool with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Metricool to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.