How to Use the Umeng / 友盟+ MCP in Pydantic AI

Q: Can I list past campaigns using Umeng / 友盟+ and Pydantic AI?

Yes, the agent can call listpushtasks and receive a structured list of recent activities. This means you don't have to write custom parsing code.

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Connect Umeng / 友盟+ MCP to Pydantic AI

Create your Vinkius account to connect Umeng / 友盟+ 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 Umeng / 友盟+ with Pydantic AI

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Perplexity

Execute reliable push campaigns.

You can send notifications via `send_push`, but the key here is that the response is guaranteed correct. The agent validates every action, whether it's sending a message or canceling one using `cancel_push`. This eliminates silent failures because if Umeng / 友盟+ sends unexpected data, your Pydantic AI setup fails loudly with an error.

Obtain validated user metrics.

The agent queries the platform for active users using `get_active_users`. Furthermore, it can retrieve detailed user retention statistics via `get_retention`. Every single output is strictly typed and checked against your defined schema.

Audit campaign status precisely.

You need to know what happened with a job? The agent checks the task status using `get_push_status` or lists tasks via `list_push_tasks`. This guarantees that when you build your system, you get structured confirmation of every marketing action.

Setup guide

Set up Umeng / 友盟+ 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": {
        "umeng-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Umeng / 友盟+ transactions")
print(result.output)

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Common questions about Umeng / 友盟+ MCP in Pydantic AI

The framework validates every single response from the MCP Server against your defined types. When you run a tool like `get_app_summary`, you're guaranteed to get data that matches your expectations.

Yes, the agent can call `list_push_tasks` and receive a structured list of recent activities. This means you don't have to write custom parsing code.

The server gives access to core stats like new registrations (`get_new_users`) and detailed analytics via `get_event_data`. All this data comes back in a predictable, typed format.

It's built around correctness. If the underlying Umeng / 友盟+ API changes its output structure, your agent fails immediately with a validation error instead of silently corrupting your logic.

The server touches user analytics data. By defining specific schemas, you control exactly what fields are exposed through tools like `get_event_data`, ensuring only necessary data is processed.

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