How to Use the Chuanglan 253 / 创蓝 MCP in Pydantic AI

Q: How do I initialize the Chuanglan 253 / 创蓝 MCP server in Pydantic AI?

Install pydantic-ai-slimmcp and create an MCPToolset pointing to your Vinkius HTTP endpoint. Pass this directly into the toolsets array of your Agent. Do not use the deprecated MCPServerHTTP class.

Q: Can I use local models with the Chuanglan 253 / 创蓝 MCP server in Pydantic AI?

Yes. Pydantic AI is model-agnostic. You can route verifyphone requests through a local Llama instance just as easily as you would through OpenAI or Anthropic.

Bring strict type validation to the Chuanglan 253 / 创蓝 MCP Server within your Pydantic AI agents.

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Connect Chuanglan 253 / 创蓝 MCP to Pydantic AI

Create your Vinkius account to connect Chuanglan 253 / 创蓝 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 Chuanglan 253 / 创蓝 with Pydantic AI

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Claude

Perplexity

Type-Safe Identity Verification

This server handles strict KYC validation so your agent can verify ID cards against official records. When dealing with sensitive data, silent failures ruin databases. Your agent calls `verify_identity` to check credentials against telecom directories. Pydantic AI validates the response structure before your application ever sees the data. If the provider returns a malformed payload or an unexpected error code, the framework throws a validation error immediately. You catch the exception and halt the onboarding process rather than saving corrupt states.

Deterministic SMS Dispatch

This integration executes deterministic SMS dispatch through strict schemas. You need absolute certainty when firing off text messages. The agent executes `send_sms` or `send_variable_sms` using models that guarantee the payload includes the exact required parameters. There's no guessing if the message actually went out. The agent runs `query_sms_status`, and Pydantic AI forces the result into a predictable model. If the delivery status field is missing, the system fails loudly so you can intervene.

Chuanglan 253 / 创蓝 MCP Server Audits

The server exposes account management tools so your agent can audit credit balances and delivery logs. Account management requires precision. Your agent checks `get_balance` on a schedule. Because Pydantic AI enforces the expected float or integer types, you never accidentally parse a string error message as a valid credit balance. For historical data, the agent invokes `pull_sms_reports`. You define the exact schema for the report rows. Any hallucinated fields from the LLM or structural changes from the API get caught at runtime.

Setup guide

Set up Chuanglan 253 / 创蓝 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": {
        "chuanglan-253-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Chuanglan 253 / 创蓝 tools.",
)

result = await agent.run("List recent Chuanglan 253 / 创蓝 transactions")
print(result.output)

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Common questions about Chuanglan 253 / 创蓝 MCP in Pydantic AI

Install pydantic-ai-slim[mcp] and create an MCPToolset pointing to your Vinkius HTTP endpoint. Pass this directly into the toolsets array of your Agent. Do not use the deprecated MCPServerHTTP class.

The framework guarantees the output matches your Pydantic models. When the agent calls `pull_sms_reports`, any deviation from the expected JSON structure throws an immediate validation exception.

Yes. Pydantic AI is model-agnostic. You can route `verify_phone` requests through a local Llama instance just as easily as you would through OpenAI or Anthropic.

Define a strict Pydantic model for your template variables. When the agent prepares to call `send_variable_sms`, the framework ensures no required template fields are missing before executing the request.

Vinkius provisions ephemeral environments for every execution. Government ID numbers and names passed to the verification tools exist only for the duration of the HTTP request before the container terminates.

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