Lamha MCP Server for LlamaIndexGive LlamaIndex instant access to 8 tools to Cancel Order, Check City Coverage, Create Order, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Lamha as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Lamha app connector for LlamaIndex 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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Lamha. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Lamha?"
)
print(response)
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.
LlamaIndex agents combine Lamha tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex
When LlamaIndex 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 LlamaIndex via MCP
Follow these steps to wire Lamha into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Lamha MCP Server
LlamaIndex provides unique advantages when paired with Lamha through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Lamha tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Lamha tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Lamha, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Lamha tools were called, what data was returned, and how it influenced the final answer
Lamha + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Lamha MCP Server delivers measurable value.
Hybrid search: combine Lamha real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Lamha to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Lamha for fresh data
Analytical workflows: chain Lamha queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Lamha in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Lamha to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLamha + LlamaIndex FAQ
Common questions about integrating Lamha MCP Server with LlamaIndex.