How to Use the Adobe Analytics MCP in Pydantic AI

Q: How do I connect Adobe Analytics to Pydantic AI?

Install the pydantic-ai-slimmcp package. Initialize an MCPToolset with your Vinkius HTTP endpoint and pass it to the agent's toolsets list.

Enforce strict runtime validation for your Adobe Analytics data using Pydantic AI.

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Connect Adobe Analytics MCP to Pydantic AI

Create your Vinkius account to connect Adobe Analytics to Pydantic AI 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 Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Fail loudly on bad reports

Silent data corruption ruins marketing dashboards. When your agent calls `get_report`, the framework validates the incoming JSON against your predefined Pydantic models at runtime. Unexpected fields trigger immediate validation errors. The agent stops execution rather than hallucinating numbers based on a malformed API response.

Type-safe Adobe Analytics MCP Server requests

Complex reporting requires exact parameter matching. Running `get_dimensions` or `get_metrics` returns specific schema requirements that the agent must follow. Model-agnostic design lets you swap LLMs without rewriting validation logic. The type safety remains intact across Anthropic, OpenAI, or local models.

Manage audience segments securely

Pulling target lists demands accuracy. Executing `list_segments` returns audience data that maps directly to your Python dataclasses. Finding the right property starts with `list_report_suites`. You pass the unified MCPToolset to your agent, ensuring all tool calls route through the external HTTP connection.

Setup guide

Set up Adobe Analytics MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "adobe-analytics-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Adobe Analytics tools.",
)

result = await agent.run("List recent Adobe Analytics transactions")
print(result.output)

Get your connection token →

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 Pydantic AI

Install the pydantic-ai-slim[mcp] package. Initialize an MCPToolset with your Vinkius HTTP endpoint and pass it to the agent's toolsets list.

The returned JSON from the API likely violated your schema definition. Check the validation error traceback to see which field caused the strict typing failure.

Yes. The framework separates the tool execution from the language model. You can run a local open-weights model against this MCP Server safely.

No, that approach is deprecated. You must use the unified MCPToolset approach to connect external MCP endpoints.

Vinkius manages authentication externally so your code never touches the raw credentials. Device types and page views are processed in-memory and discarded the moment the HTTP session closes.

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