Adobe Analytics MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Adobe Analytics as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Adobe Analytics. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in Adobe Analytics?"
)
print(response)
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 Adobe Analytics MCP Server
Connect your Adobe Analytics account to your AI agent to unlock deep customer journey insights and real-time data orchestration. From retrieving complex reporting breakdowns to managing audience segments and auditing calculated metrics, your agent handles your enterprise analytics ecosystem through natural conversation.
LlamaIndex agents combine Adobe Analytics tool responses with indexed documents for comprehensive, grounded answers. Connect 5 tools through the 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
- Enterprise Reporting — Retrieve synchronous reports with nested breakdowns and complex filters directly from chat
- Component Discovery — List and audit all available metrics and dimensions for your specific report suites
- Segment Management — List and retrieve details for audience segments to ensure your data is always relevant
- Report Suite Oversight — Manage and list your report suites (collections) to maintain organizational control
- Real-time Performance — Quickly identify traffic trends and engagement patterns without manual dashboard configuration
The Adobe Analytics 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 Adobe Analytics to LlamaIndex via MCP
Follow these steps to integrate the Adobe Analytics 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 Adobe Analytics
Why Use LlamaIndex with the Adobe Analytics MCP Server
LlamaIndex provides unique advantages when paired with Adobe Analytics through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Adobe Analytics tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Adobe Analytics tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Adobe Analytics, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Adobe Analytics tools were called, what data was returned, and how it influenced the final answer
Adobe Analytics + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Adobe Analytics MCP Server delivers measurable value.
Hybrid search: combine Adobe Analytics real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Adobe Analytics 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 Adobe Analytics for fresh data
Analytical workflows: chain Adobe Analytics queries with LlamaIndex's data connectors to build multi-source analytical reports
Adobe Analytics MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect Adobe Analytics to LlamaIndex via MCP:
get_dimensions
g. Page, Device Type) for a specific report suite ID. List dimensions for a report suite
get_metrics
List metrics for a report suite
get_report
0 JSON report request body. Retrieve an analytics report
list_report_suites
List available report suites
list_segments
List audience segments
Example Prompts for Adobe Analytics in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Adobe Analytics immediately.
"List all metrics available for report suite 'mycompany-prod'."
"Show me the top 5 pages by visits for yesterday."
"List all active segments in my Adobe Analytics account."
Troubleshooting Adobe Analytics MCP Server with LlamaIndex
Common issues when connecting Adobe Analytics to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAdobe Analytics + LlamaIndex FAQ
Common questions about integrating Adobe Analytics MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Adobe Analytics 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 Adobe Analytics to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.