AfterLogic Aurora MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AfterLogic Aurora 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 AfterLogic Aurora. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in AfterLogic Aurora?"
)
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 AfterLogic Aurora MCP Server
Connect your AfterLogic Aurora account to your AI agent to unlock professional email and webmail orchestration. From managing complex mail folder structures to retrieving message lists and handling administrative account tasks, your agent handles your communication platform through natural conversation.
LlamaIndex agents combine AfterLogic Aurora tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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
- Mail Orchestration — List and manage email folders and retrieve message lists for any account
- Administrative Management — Check if accounts exist and manage domains or users via the REST Admin API
- Communication Flow — Send and retrieve technical metadata for emails to support your communication workflows
- Integration Support — Access both the Web API for user-level tasks and the REST API for system-wide administration
- Status Monitoring — Quickly audit mail server availability and account statuses directly from your chat interface
The AfterLogic Aurora MCP Server exposes 5 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 AfterLogic Aurora to LlamaIndex via MCP
Follow these steps to integrate the AfterLogic Aurora 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 5 tools from AfterLogic Aurora
Why Use LlamaIndex with the AfterLogic Aurora MCP Server
LlamaIndex provides unique advantages when paired with AfterLogic Aurora through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AfterLogic Aurora tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AfterLogic Aurora tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AfterLogic Aurora, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AfterLogic Aurora tools were called, what data was returned, and how it influenced the final answer
AfterLogic Aurora + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AfterLogic Aurora MCP Server delivers measurable value.
Hybrid search: combine AfterLogic Aurora real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AfterLogic Aurora 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 AfterLogic Aurora for fresh data
Analytical workflows: chain AfterLogic Aurora queries with LlamaIndex's data connectors to build multi-source analytical reports
AfterLogic Aurora MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect AfterLogic Aurora to LlamaIndex via MCP:
check_account_exists
Requires Admin rights. Verify if an email address is actively provisioned on the AfterLogic server
list_domains
Requires Admin rights. Retrieve all active custom domains mapped to the AfterLogic server instance
list_folders
Retrieve the internal email folder hierarchy for the authenticated AfterLogic user
list_messages
Requires a folder path from list_folders. Retrieve recent emails contained within a specified AfterLogic mail folder
send_email
Compose and send an outbound email securely via the AfterLogic Web API
Example Prompts for AfterLogic Aurora in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AfterLogic Aurora immediately.
"List all mail folders for user 'admin@example.com'."
"Check if the account 'user1@example.com' exists on the server."
"Show me the last 10 messages in the INBOX."
Troubleshooting AfterLogic Aurora MCP Server with LlamaIndex
Common issues when connecting AfterLogic Aurora to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAfterLogic Aurora + LlamaIndex FAQ
Common questions about integrating AfterLogic Aurora 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 AfterLogic Aurora 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 AfterLogic Aurora to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.