Condeco (Eptura Engage) 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 Condeco (Eptura Engage) 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 Condeco (Eptura Engage). "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Condeco (Eptura Engage)?"
)
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 Condeco (Eptura Engage) MCP Server
Connect your Condeco (now Eptura Engage) account to any AI agent and take full control of your enterprise workspace and desk booking workflows through natural conversation.
LlamaIndex agents combine Condeco (Eptura Engage) 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
- Office Location Navigation — Identify bounded office capacities and geographic groupings mapping explicitly tracked buildings and campuses
- Room Management — Enumerate available meeting spaces, filtering by capacity and AV features, and check real-time usage states
- Desk & Hot Desking — Claim exclusive usage of hot desks within specific neighborhoods and zones, including equipment filters
- Live Reservations — Mutate active scheduling endpoints to book or cancel rooms and desks with instant sync to O365/Exchange
- Check-in Automation — Trigger physical presence capabilities to confirm your arrival at a location and satisfy local access controls
- Booking History — Extract chronological logs of user reservations to audit space utilization across your organization
The Condeco (Eptura Engage) 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 Condeco (Eptura Engage) to LlamaIndex via MCP
Follow these steps to integrate the Condeco (Eptura Engage) 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 Condeco (Eptura Engage)
Why Use LlamaIndex with the Condeco (Eptura Engage) MCP Server
LlamaIndex provides unique advantages when paired with Condeco (Eptura Engage) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Condeco (Eptura Engage) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Condeco (Eptura Engage) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Condeco (Eptura Engage), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Condeco (Eptura Engage) tools were called, what data was returned, and how it influenced the final answer
Condeco (Eptura Engage) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Condeco (Eptura Engage) MCP Server delivers measurable value.
Hybrid search: combine Condeco (Eptura Engage) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Condeco (Eptura Engage) 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 Condeco (Eptura Engage) for fresh data
Analytical workflows: chain Condeco (Eptura Engage) queries with LlamaIndex's data connectors to build multi-source analytical reports
Condeco (Eptura Engage) MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Condeco (Eptura Engage) to LlamaIndex via MCP:
book_desk
Claim exclusive usage targeting specific hot desking bounds
book_room
Claim exclusive scheduling capabilities tracing synchronization triggers upon physical spaces
cancel_desk_booking
Revoke claimed exclusive desk reservations natively
cancel_room_booking
Revoke exact chronological reservations matching O365 sync boundaries
check_in_to_location
Trigger physical presence capabilities executing explicit local access controls
get_room_availability
Determine real-time usage states extracting explicit chronological meeting blocks
list_bookings
Extract chronological logs resolving explicit user booking reservations
list_desks
Identify specific bounding capacities covering mapped hot desks
list_locations
Identify bounded office capacities discovering standard enterprise real estate limits
list_rooms
Enumerate explicitly mapped meeting spaces filtering capacity and feature sets
Example Prompts for Condeco (Eptura Engage) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Condeco (Eptura Engage) immediately.
"List all meeting rooms in the 'New York HQ' location"
"Book room 101 for tomorrow from 2:00 PM to 3:00 PM with title 'Weekly Sync'"
"I've arrived at the office. Check me in to location 50"
Troubleshooting Condeco (Eptura Engage) MCP Server with LlamaIndex
Common issues when connecting Condeco (Eptura Engage) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCondeco (Eptura Engage) + LlamaIndex FAQ
Common questions about integrating Condeco (Eptura Engage) 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 Condeco (Eptura Engage) 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 Condeco (Eptura Engage) to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.