How do I get my Adikteev API account?

You must contact your Adikteev Account Manager to have them enable API access for your account. Once enabled, you can use your standard login credentials (email and password) to authorize the agent.

What is a Churn Score?

A churn score ranks app users by their probability of leaving. Adikteev provides these in 'buckets' from 1 to 10, where 10 represents the highest risk of churn.

How do I create a new audience segment?

Use the `create_segment` tool and provide your `companyId`, a name, and a description. Your agent will create the segment in Adikteev and provide a `segmentId` for device ID uploads.

Adikteev MCP Server for LlamaIndex 5 tools — connect in under 2 minutes

Name: Adikteev
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 5 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Adikteev as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Adikteev. "
            "You have 5 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Adikteev?"
    )
    print(response)

asyncio.run(main())

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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.

LlamaIndex agents combine Adikteev tool responses with indexed documents for comprehensive, grounded answers. Connect 5 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

Follow these steps to integrate the Adikteev MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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 5 tools from Adikteev

Why Use LlamaIndex with the Adikteev MCP Server

LlamaIndex provides unique advantages when paired with Adikteev through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Adikteev tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Adikteev tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Adikteev, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Adikteev tools were called, what data was returned, and how it influenced the final answer

Adikteev + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Adikteev MCP Server delivers measurable value.

Hybrid search: combine Adikteev real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Adikteev to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Adikteev for fresh data

Analytical workflows: chain Adikteev queries with LlamaIndex's data connectors to build multi-source analytical reports

Adikteev MCP Tools for LlamaIndex (5)

These 5 tools become available when you connect Adikteev to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

Common issues when connecting Adikteev to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Adikteev + LlamaIndex FAQ

Common questions about integrating Adikteev MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Adikteev tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.