Lamha MCP Server for Pydantic AIGive Pydantic AI instant access to 8 tools to Cancel Order, Check City Coverage, Create Order, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Lamha through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this App Connector for Pydantic AI
The Lamha app connector for Pydantic AI is a standout in the Productivity category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Lamha "
"(8 tools)."
),
)
result = await agent.run(
"What tools are available in Lamha?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Lamha MCP Server
Connect your Lamha account to any AI agent and manage HR operations through natural conversation.
Pydantic AI validates every Lamha tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Employee Management — List employees, inspect profiles, and track status
- Attendance Tracking — Monitor check-in/out times and attendance records
- Department Browsing — Navigate organizational structure and departments
- Leave Management — Track leave requests, balances, and approvals
- Payroll Access — View payroll data and compensation details
The Lamha MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 8 Lamha tools available for Pydantic AI
When Pydantic AI connects to Lamha through Vinkius, your AI agent gets direct access to every tool listed below — spanning attendance-tracking, leave-management, payroll-management, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Cancel an existing order
Check delivery coverage for a city
Create a new logistics order
Get details for a specific order
List delivery carriers
List product inventory
List Lamha orders
List warehouses
Connect Lamha to Pydantic AI via MCP
Follow these steps to wire Lamha into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the Lamha MCP Server
Pydantic AI provides unique advantages when paired with Lamha through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Lamha integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Lamha connection logic from agent behavior for testable, maintainable code
Lamha + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Lamha MCP Server delivers measurable value.
Type-safe data pipelines: query Lamha with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Lamha tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Lamha and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Lamha responses and write comprehensive agent tests
Example Prompts for Lamha in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Lamha immediately.
"Show all departments and today's attendance."
"Show pending leave requests and employee leave balances."
"Show payroll summary and employee details for the Engineering team."
Troubleshooting Lamha MCP Server with Pydantic AI
Common issues when connecting Lamha to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiLamha + Pydantic AI FAQ
Common questions about integrating Lamha MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.