ItemPath MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ItemPath as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 ItemPath. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in ItemPath?"
)
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 ItemPath MCP Server
Empower your AI agents to manage your warehouse and inventory with ItemPath. This MCP server allows you to list materials, retrieve order details, track inventory transactions, and view storage locations directly through the ItemPath API. Ideal for automating supply chain operations and stock monitoring.
LlamaIndex agents combine ItemPath tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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.
The ItemPath MCP Server exposes 10 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.
How to Connect ItemPath to LlamaIndex via MCP
Follow these steps to integrate the ItemPath MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from ItemPath
Why Use LlamaIndex with the ItemPath MCP Server
LlamaIndex provides unique advantages when paired with ItemPath through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ItemPath tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ItemPath tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ItemPath, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ItemPath tools were called, what data was returned, and how it influenced the final answer
ItemPath + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ItemPath MCP Server delivers measurable value.
Hybrid search: combine ItemPath real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ItemPath 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 ItemPath for fresh data
Analytical workflows: chain ItemPath queries with LlamaIndex's data connectors to build multi-source analytical reports
ItemPath MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect ItemPath to LlamaIndex via MCP:
get_material
Returns SKU details, storage rules, and quantity-on-hand. Essential for analyzing the status of specific inventory items. Retrieves details for a specific material
get_me
Use to verify connection health and current user identity. Gets current authenticated user info
get_order
Returns the list of materials involved, target locations, and picker information. Use this for troubleshooting order fulfillment or providing status updates. Retrieves details for a specific order
list_batches
Essential for managing perishable goods or regulated materials requiring lot tracking. Lists all material batches
list_calls
Useful for debugging integrations and monitoring system interaction frequency. Lists recent API request history
list_locations
Useful for understanding warehouse layout and identifying where specific materials are stored. Lists all storage locations
list_materials
Returns material names, descriptions, and IDs. Use this to identify products for inventory auditing or order analysis. Lists all materials in ItemPath
list_orders
Includes order IDs, types, and current status. Essential for monitoring warehouse throughput and workflow. Lists all orders
list_transactions
Returns timestamps, material IDs, quantity changes, and user IDs. Essential for auditing inventory accuracy and identifying recent stock changes. Lists all inventory transactions
list_users
Useful for identifying who performed specific inventory transactions. Lists all system users
Example Prompts for ItemPath in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ItemPath immediately.
"List all active materials in the warehouse."
"Show me the details for order ID 'ORD-123'."
"Check recent inventory transactions."
Troubleshooting ItemPath MCP Server with LlamaIndex
Common issues when connecting ItemPath to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpItemPath + LlamaIndex FAQ
Common questions about integrating ItemPath MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect ItemPath with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ItemPath to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.