How to Use the Baidu Analytics / 百度统计 MCP in Pydantic AI

Q: How do I configure the Baidu Analytics / 百度统计 MCP Server with Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your Vinkius URL and add it to your Agent's toolsets list. The server must run externally, which Vinkius handles for you.

Type-safe Baidu Analytics integration. Connect Pydantic AI to validate every traffic metric at runtime.

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Connect Baidu Analytics / 百度统计 MCP to Pydantic AI

Create your Vinkius account to connect Baidu Analytics / 百度统计 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 Baidu Analytics / 百度统计 with Pydantic AI

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Claude

Perplexity

Enforce Strict Schema Validation

Strict schema validation guarantees that all analytics metrics match your expected Python types. Analytics APIs notoriously return unexpected nulls or change data formats. Pydantic AI solves this by validating the MCP Server output against your exact models. You never have to worry about silent data corruption in your reporting pipeline. When your agent calls `get_daily_trend`, the framework guarantees the response matches your defined Python classes before any downstream logic executes.

Configure the Baidu Analytics MCP Server

The `baidu-analytics-mcp` setup connects your Vinkius endpoint through the unified toolset class. You instantiate an `MCPToolset` with your HTTP endpoint and pass it to your Agent. The deprecated `MCPServerHTTP` class is gone, making the initialization much cleaner. You can swap between Claude, Gemini, or a local model at will. The agent pulls regional data via `get_geo_distribution` and structures the output exactly the same way regardless of the backend.

Execute Complex Custom Queries

Custom report execution handles the specific dimension and metric combinations required by the API. Standard reports rarely answer specific business questions. The `query_custom_metrics` tool lets your agent construct and run highly specific queries against your raw data. Managing multiple properties happens through the `list_sites` operation. Your agent grabs the master list, then systematically checks `get_source_data` across every domain to build a consolidated cross-site acquisition report.

Setup guide

Set up Baidu Analytics / 百度统计 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": {
        "baidu-analytics-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Baidu Analytics / 百度统计 tools.",
)

result = await agent.run("List recent Baidu Analytics / 百度统计 transactions")
print(result.output)

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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 Baidu Analytics / 百度统计. 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 Baidu Analytics / 百度统计 MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Create an `MCPToolset` pointing to your Vinkius URL and add it to your Agent's `toolsets` list. The server must run externally, which Vinkius handles for you.

Runtime validation is the main draw. When your agent fetches `get_page_rankings`, Pydantic ensures the URLs and view counts perfectly match your expected schema, preventing hallucinated fields from breaking your app.

Yes, Pydantic AI supports both Streamable HTTP and SSE transports for the toolset. This keeps the connection open and fast when the agent needs to make sequential calls like checking `get_yesterday_overview`.

The framework is entirely model-agnostic. You pipe the output from `get_realtime_metrics` into a local Llama instance just as easily as you send it to OpenAI.

Vinkius handles authentication at the edge and runs the connection in an isolated V8 container. Your custom report queries, active user counts, and domain lists are never logged or stored on our infrastructure.

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