Envoy 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 Envoy 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 Envoy. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Envoy?"
)
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 Envoy MCP Server
Connect your Envoy workplace account to any AI agent and take full control of your office management and visitor registration through natural conversation.
LlamaIndex agents combine Envoy 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
- Visitor Orchestration — Register expected arrivals and deliver QR code invites seamlessly while mapping NDA tracking and security compliance natively
- Hot Desk Management — List all available desks and reserve physical workspace elements by committing exact timing payloads directly into the organizational map
- Meeting Room Control — Identify bookable rooms and spaces, calculating maximal volumetric tracking and reporting integration limits securely
- Logistical Tracking — Monitor incoming deliveries and package states, extracting pickup receipts and bypassing front desk barriers flawlessly
- Office Capacity Auditing — Measure real-time occupancy metrics and compute active relational loads to ensure workplace compliance bounding
- Employee Presence Monitoring — Analyze specific HR identity connections fetching log trails to validate physical office sign-ins across any date range
- Location Navigation — Iterate through global office locations and workspaces to parse precise geographic configurations and maximum capacity limits
The Envoy 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 Envoy to LlamaIndex via MCP
Follow these steps to integrate the Envoy 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 Envoy
Why Use LlamaIndex with the Envoy MCP Server
LlamaIndex provides unique advantages when paired with Envoy through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Envoy tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Envoy tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Envoy, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Envoy tools were called, what data was returned, and how it influenced the final answer
Envoy + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Envoy MCP Server delivers measurable value.
Hybrid search: combine Envoy real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Envoy 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 Envoy for fresh data
Analytical workflows: chain Envoy queries with LlamaIndex's data connectors to build multi-source analytical reports
Envoy MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Envoy to LlamaIndex via MCP:
cancel_desk_reservation
Cancel an Envoy desk reservation
get_capacity
Get real-time capacity data for an Envoy location
get_employee_signins
Get employee sign-in data for an Envoy location
list_deliveries
List all deliveries at an Envoy location
list_desks
List all hot desks at an Envoy location
list_locations
List all office locations managed in Envoy
list_rooms
List all bookable rooms/spaces at an Envoy location
list_visitors
List all visitors checked in or expected at an Envoy location
pre_register_visitor
Pre-register a visitor in Envoy
reserve_desk
Reserve a hot desk in Envoy
Example Prompts for Envoy in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Envoy immediately.
"Pre-register guest 'Jane Doe' (jane@example.com) for tomorrow at 10 AM"
"Reserve desk 'D-101' at the 'Main Office' for next Friday"
"What is the current occupancy at the London office?"
Troubleshooting Envoy MCP Server with LlamaIndex
Common issues when connecting Envoy to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpEnvoy + LlamaIndex FAQ
Common questions about integrating Envoy 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 Envoy 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 Envoy to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.