Arrivy MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Arrivy 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 Arrivy. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in Arrivy?"
)
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 Arrivy MCP Server
The Arrivy MCP Server empowers your AI agent to coordinate field operations and last-mile delivery directly from your workspace. Seamlessly manage your mobile workforce, track job progress, and engage with customers using natural language.
LlamaIndex agents combine Arrivy tool responses with indexed documents for comprehensive, grounded answers. Connect 9 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.
Key Features
- Task Orchestration — List, create, and update service tasks or delivery jobs with real-time status tracking.
- Crew Management — Monitor field personnel and resource assignments to ensure efficient job allocation.
- Customer Engagement — Manage customer records and sync service history for better communication.
- Location Tracking — Access real-time location data and ETAs for your field technicians and delivery drivers.
- Digital Workflow — Access data captured in the field, including forms, photos, and status updates.
- Seamless Integration — Connect your Arrivy operations with your AI-assisted project management and support workflows.
Benefits for Teams
- Operations Managers — Quickly audit active jobs and crew statuses without leaving your AI dashboard.
- Dispatchers — Use AI to quickly create and assign new tasks based on customer requests.
- Customer Success — Retrieve job history and ETAs instantly to provide accurate updates to clients.
The Arrivy MCP Server exposes 9 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 Arrivy to LlamaIndex via MCP
Follow these steps to integrate the Arrivy 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 9 tools from Arrivy
Why Use LlamaIndex with the Arrivy MCP Server
LlamaIndex provides unique advantages when paired with Arrivy through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Arrivy tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Arrivy tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Arrivy, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Arrivy tools were called, what data was returned, and how it influenced the final answer
Arrivy + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Arrivy MCP Server delivers measurable value.
Hybrid search: combine Arrivy real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Arrivy 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 Arrivy for fresh data
Analytical workflows: chain Arrivy queries with LlamaIndex's data connectors to build multi-source analytical reports
Arrivy MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect Arrivy to LlamaIndex via MCP:
create_customer
Create a new customer record
create_task
Create a new service task in Arrivy
get_account_check
Verify Arrivy account connection
get_task
Get details for a specific task
list_crews
List all field crews and personnel
list_customers
List all customers in the system
list_locations
List all tracked locations
list_tasks
List all service tasks in Arrivy
update_task
Update an existing service task
Example Prompts for Arrivy in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Arrivy immediately.
"List all scheduled tasks for today in Arrivy."
"Create a new task 'Emergency Leak Repair' at '123 Maple St'."
"Show me the status of task ID 'T12345'."
Troubleshooting Arrivy MCP Server with LlamaIndex
Common issues when connecting Arrivy to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpArrivy + LlamaIndex FAQ
Common questions about integrating Arrivy 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 Arrivy 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 Arrivy to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.