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How to Use the Lamha MCP in Pydantic AI

Enforce strict typing on Lamha's MCP logistics tools using Pydantic AI.

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Pydantic AI

Connect Lamha MCP to Pydantic AI

Create your Vinkius account to connect Lamha 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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Validate logistics data in Pydantic AI

Lamha equips your agent with `get_order` and `list_orders` tools that must pass strict runtime validation. If the API returns a missing tracking number, your Python code fails loudly instead of passing bad data. You build e-commerce bots that actually work. The agent maps complex Arabic dialect queries into exact Pydantic models before ever touching the network.

Route shipments via the MCP Server

The model queries `check_city_coverage` and `list_carriers` to find delivery options in the Middle East. Every response gets checked against your predefined schemas. Hallucinated fields cause immediate validation errors. You know exactly when the agent tries to invent a carrier that doesn't exist in the region.

Read inventory with zero silent errors

Your agent pulls stock levels using `list_warehouses` and `list_inventory`. The type-safe framework guarantees the returned integers match your database expectations. Customers ask for stock updates in various Arabic dialects. The system parses the intent, fetches the exact counts, and guarantees correct formatting before replying.

Setup guide

Set up Lamha MCP in Pydantic AI

Prerequisites

  • Python 3.10+ installed
  • pydantic-ai-slim[fastmcp] package
  • Active Vinkius subscription with a valid endpoint token
  1. 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. 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. 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": {
        "lamha-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lamha. 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 Lamha MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and initialize `MCPToolset` with your Vinkius HTTP endpoint. Pass the toolset directly to your Agent constructor.
It prevents silent corruption in your e-commerce platform. When `create_order` executes, the framework guarantees the payload matches the exact schema required by Middle Eastern logistics providers.
The Lamha backend handles the natural language understanding. Your Python code just validates the structured JSON that comes back from tools like `cancel_order`.
Yes. You provide the unified HTTP endpoint to the toolset. The framework manages the underlying connection to the managed server.
Order details pulled via `get_order` exist only in memory during the validation step. The Vinkius zero-trust architecture processes names and addresses in a temporary V8 isolate. Everything vanishes when the transaction completes.

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