Adikteev MCP Server for LangChain 5 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Adikteev through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
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({
"adikteev": {
"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 Adikteev, 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 Adikteev MCP Server
Connect your Adikteev account to your AI agent to unlock professional app retargeting and user retention insights. From managing custom audience segments to monitoring campaign performance and retrieving churn probability scores, your agent handles your mobile growth ecosystem through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Adikteev through native MCP adapters. Connect 5 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
- Audience Orchestration — List, create, and manage audience segments for targeted app retargeting campaigns
- Performance Reporting — Retrieve detailed campaign performance data to monitor ROI and engagement metrics
- Churn Prediction — Access churn probability scores to identify at-risk app users before they leave your ecosystem
- Company Insights — List companies and retrieve technical metadata required for audience management
- Growth Monitoring — Quickly audit your retargeting efforts and identify high-value user segments directly from chat
The Adikteev MCP Server exposes 5 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 Adikteev to LangChain via MCP
Follow these steps to integrate the Adikteev 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 5 tools from Adikteev via MCP
Why Use LangChain with the Adikteev MCP Server
LangChain provides unique advantages when paired with Adikteev through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Adikteev 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 Adikteev queries for multi-turn workflows
Adikteev + LangChain Use Cases
Practical scenarios where LangChain combined with the Adikteev MCP Server delivers measurable value.
RAG with live data: combine Adikteev tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Adikteev, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Adikteev tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Adikteev tool call, measure latency, and optimize your agent's performance
Adikteev MCP Tools for LangChain (5)
These 5 tools become available when you connect Adikteev to LangChain via MCP:
create_segment
Create an audience segment
get_churn_scores
Retrieve user churn scores
get_reporting
Get campaign performance data
list_companies
Retrieve your company ID
list_segments
List audience segments
Example Prompts for Adikteev in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Adikteev immediately.
"List all audience segments for my company."
"Retrieve the churn scores for my app with bundle 'com.example.app'."
"Show me the performance of my retargeting campaigns."
Troubleshooting Adikteev MCP Server with LangChain
Common issues when connecting Adikteev to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersAdikteev + LangChain FAQ
Common questions about integrating Adikteev 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 Adikteev 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 Adikteev to LangChain
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.