How to Use the Adobe Analytics MCP in LlamaIndex

Q: Can I index Adobe Analytics segments in LlamaIndex?

You pull the segments using listsegments and save them into your index. This makes your segment definitions a permanent part of your agent's knowledge base.

Index your Adobe Analytics reports into LlamaIndex for grounded, searchable insights.

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Connect Adobe Analytics MCP to LlamaIndex

Create your Vinkius account to connect Adobe Analytics to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Adobe Analytics with LlamaIndex

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LlamaIndex MCP Server data ingestion

Pipe the results of `get_report` straight into your vector store. You turn transient API calls into a permanent, queryable knowledge base. Your application indexes these outputs so you can ask questions about past trends later. It prevents the agent from hallucinating figures by grounding answers in retrieved report data.

Querying Adobe Analytics via LlamaIndex

Use your existing LlamaIndex RAG pipeline to search through historical segment data. You define the tools, and the index handles the retrieval. This setup allows you to combine static documents with live analytics. Your agent searches the index first to see if a report already exists before calling the API again.

Automated report indexing

Run `list_report_suites` to discover data sources and index their metadata. You get a map of your analytics environment inside your agent's memory. This approach lets you filter available tools using the standard MCP tool list. It keeps your agent focused on the most relevant data points for your specific queries.

Setup guide

Set up Adobe Analytics MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Adobe Analytics MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Adobe Analytics tools.",
)
response = await agent.run("List recent Adobe Analytics data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Adobe Analytics. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Adobe Analytics MCP in LlamaIndex

The server provides tool outputs that LlamaIndex converts into searchable nodes. Once indexed, your agent can answer questions by querying the vector store instead of hitting the API repeatedly.

You pull the segments using `list_segments` and save them into your index. This makes your segment definitions a permanent part of your agent's knowledge base.

Yes, once you index the tool output, the data stays in your vector store. You decide when to refresh the index by re-running the tools.

It is designed to work directly with the tool spec adapter. You convert the server tools into a format the agent understands and pass them into your function-calling loop.

Data is encrypted in transit using your Vinkius-managed token. Once pulled into your index, you control the access levels and storage security of your local vector database.

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