OptimoRoute 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 OptimoRoute 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 MCP SERVER
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 OptimoRoute. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in OptimoRoute?"
)
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 OptimoRoute MCP Server
Connect your OptimoRoute account to any AI agent and orchestrate your entire route planning workflow from a single conversation.
LlamaIndex agents combine OptimoRoute 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.
What you can do
- Order Management — Create delivery orders with addresses, time windows, and capacity constraints. Delete stale orders before they get routed
- Route Optimization — Queue route planning jobs with specific dates and driver sets, then check solver status or abort long-running computations
- Route Downloads — Download finalized manifest routes showing every optimized stop assigned to each driver
- Scheduling Info — Look up exactly when and where a specific order was scheduled within the optimized plan
- Proof of Delivery — Retrieve completion details including driver signatures, photos, and notes for verification
- Live GPS Tracking — Get real-time GPS coordinates for any driver in your fleet
- Driver Configuration — Update working hours, speed profiles, and load capacities for your drivers
The OptimoRoute 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 OptimoRoute to LlamaIndex via MCP
Follow these steps to integrate the OptimoRoute 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 OptimoRoute
Why Use LlamaIndex with the OptimoRoute MCP Server
LlamaIndex provides unique advantages when paired with OptimoRoute through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine OptimoRoute tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain OptimoRoute tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query OptimoRoute, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what OptimoRoute tools were called, what data was returned, and how it influenced the final answer
OptimoRoute + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the OptimoRoute MCP Server delivers measurable value.
Hybrid search: combine OptimoRoute real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query OptimoRoute 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 OptimoRoute for fresh data
Analytical workflows: chain OptimoRoute queries with LlamaIndex's data connectors to build multi-source analytical reports
OptimoRoute MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect OptimoRoute to LlamaIndex via MCP:
abort_route_planning
Inspect deep internal arrays mitigating specific Time constraints
check_planning_status
Identify precise active arrays spanning native Asynchronous bounds
create_route_order
Identify bounded routing spaces inside the Headless OptimoRoute platform
delete_stale_order
Perform structural extraction of properties driving active Deletions
download_manifest_routes
Enumerate explicitly attached structured rules exporting active Drivers
get_live_driver_gps
Irreversibly vaporize explicit validations extracting Telemetry natively
get_order_pod
Retrieve the exact structural matching verifying Delivery success Proof
get_order_scheduling
Dispatch an automated validation check routing explicit ETA models
queue_route_optimization
Retrieve explicit Cloud logging tracing explicit solver logic
update_driver_shifts
Provision a highly-available JSON Payload generating Truck constraints
Example Prompts for OptimoRoute in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with OptimoRoute immediately.
"Create a delivery order for 200 Main St, Boston with a 2-hour time window from 9 AM to 11 AM."
"Start route optimization for tomorrow with drivers D1, D2, and D3."
"Where is driver D2 right now?"
Troubleshooting OptimoRoute MCP Server with LlamaIndex
Common issues when connecting OptimoRoute to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOptimoRoute + LlamaIndex FAQ
Common questions about integrating OptimoRoute 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 OptimoRoute 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 OptimoRoute to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.