join.me MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Create Webhook, Delete Meeting, Get Meeting, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add join.me 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 App Connector for LlamaIndex
The join.me app connector for LlamaIndex is a standout in the Communication Messaging category — giving your AI agent 10 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 join.me. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in join.me?"
)
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 join.me MCP Server
Connect your join.me account to any AI agent and manage video meetings through natural conversation.
LlamaIndex agents combine join.me 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
- Meeting Scheduling — Create, update, and cancel meetings with customizable settings
- Participant Management — Invite participants, track attendance, and manage access
- Recording Access — List and retrieve meeting recordings
- Meeting History — Browse past meetings with duration and participant data
- Settings Configuration — Manage account and meeting preferences
The join.me 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.
All 10 join.me tools available for LlamaIndex
When LlamaIndex connects to join.me through Vinkius, your AI agent gets direct access to every tool listed below — spanning screen-sharing, meeting-scheduling, recording, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Register a new webhook
Cancel/Delete a meeting
Get details of a specific meeting
me user profile. Get account information
me account. List your join.me meetings
me account. List registered webhooks
Schedule a future meeting
Start an instant meeting
Start a scheduled meeting
Update a scheduled meeting
Connect join.me to LlamaIndex via MCP
Follow these steps to wire join.me into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the join.me MCP Server
LlamaIndex provides unique advantages when paired with join.me through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine join.me tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain join.me tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query join.me, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what join.me tools were called, what data was returned, and how it influenced the final answer
join.me + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the join.me MCP Server delivers measurable value.
Hybrid search: combine join.me real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query join.me 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 join.me for fresh data
Analytical workflows: chain join.me queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for join.me in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with join.me immediately.
"Schedule a team standup for tomorrow at 9 AM and invite the engineering team."
"Show this week's meeting history and list available recordings."
"Cancel tomorrow's 3 PM meeting and update the standup to 9:30 AM."
Troubleshooting join.me MCP Server with LlamaIndex
Common issues when connecting join.me to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpjoin.me + LlamaIndex FAQ
Common questions about integrating join.me MCP Server with LlamaIndex.