Where do I find my Global Company ID?

The easiest way is to log in to the Adobe Developer Console and check your project settings. Alternatively, once you have an access token, the `list_report_suites` tool will return your authorized company metadata.

Are reports returned in real-time?

Yes. Unlike older versions of the API, the v2.0 Reporting API is synchronous and returns the data directly in the request response, making it perfect for AI interactions.

Does this support complex breakdowns?

Yes, the `get_report` tool accepts a JSON payload that supports the full range of Adobe Analytics v2.0 reporting features, including nested breakdowns and metric filters.

Adobe Analytics MCP Server for Pydantic AI 5 tools — connect in under 2 minutes

Name: Adobe Analytics
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 5 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Adobe Analytics through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Adobe Analytics "
            "(5 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Adobe Analytics?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

Pydantic AI validates every Adobe Analytics tool response against typed schemas, catching data inconsistencies at build time. Connect 5 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Adobe Analytics MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Adobe Analytics MCP Server

Pydantic AI provides unique advantages when paired with Adobe Analytics through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Adobe Analytics integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Adobe Analytics connection logic from agent behavior for testable, maintainable code

Adobe Analytics + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Adobe Analytics MCP Server delivers measurable value.

Type-safe data pipelines: query Adobe Analytics with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Adobe Analytics tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Adobe Analytics and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Adobe Analytics responses and write comprehensive agent tests

Adobe Analytics MCP Tools for Pydantic AI (5)

These 5 tools become available when you connect Adobe Analytics to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Adobe Analytics to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Adobe Analytics + Pydantic AI FAQ

Common questions about integrating Adobe Analytics MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Adobe Analytics MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.