Does my AI agent need a preexisting Gmail or Outlook account to use this?

No. AgentMail is entirely standalone. You can use your agent to programmatically provision brand new, unique email addresses in seconds, completely independent of legacy email providers.

Can my agent download files that users send to its email address?

Yes. When scanning threads in the inbox, your agent will detect any attached files. It can then use the dedicated attachments tool to retrieve and analyze the contents of the file before drafting a reply.

Is it possible to manage multiple outreach campaigns at the same time?

Absolutely. Your agent can create and manage multiple inboxes simultaneously. It can list all active boxes, read unread threads for each, and contextualize its replies based on the specific campaign inbox.

AgentMail MCP Server for LlamaIndex 11 tools — connect in under 2 minutes

Name: AgentMail
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 11 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AgentMail as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 AgentMail. "
            "You have 11 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in AgentMail?"
    )
    print(response)

asyncio.run(main())

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* 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 AgentMail MCP Server

Connect AgentMail to your AI agent and unlock a programmable email client. Stop relying on complex integrations and grant your agent its own functional inbox to communicate with the world.

LlamaIndex agents combine AgentMail tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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

Inboxes — Create, list, and delete custom email addresses on the fly for your agent
Threads — Scan active conversations and read full historical threads natively
Messages — Send new emails, reply contextually to specific threads, and forward messages
Attachments — Extract and process files attached to incoming emails automatically

The AgentMail MCP Server exposes 11 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 AgentMail to LlamaIndex via MCP

Follow these steps to integrate the AgentMail 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 11 tools from AgentMail

Why Use LlamaIndex with the AgentMail MCP Server

LlamaIndex provides unique advantages when paired with AgentMail through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine AgentMail tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain AgentMail tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query AgentMail, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what AgentMail tools were called, what data was returned, and how it influenced the final answer

AgentMail + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the AgentMail MCP Server delivers measurable value.

Hybrid search: combine AgentMail real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query AgentMail 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 AgentMail for fresh data

Analytical workflows: chain AgentMail queries with LlamaIndex's data connectors to build multi-source analytical reports

AgentMail MCP Tools for LlamaIndex (11)

These 11 tools become available when you connect AgentMail to LlamaIndex via MCP:

create_inbox

You can optionally link it to a custom domain. Create a new email inbox for an agent

delete_inbox

Warning: this deletes all emails in it. Delete a specific inbox by ID

forward_message

You can optionally add text to the forwarded message. Forward an existing email message

get_attachment

Attachments might be encoded in base64. Ensure you parse or read it correctly. Download or read a specific attachment from a message

get_inbox

Get details of a specific inbox by ID

get_thread

Requires a thread_id. Read all messages inside a specific conversation thread

list_inboxes

An inbox is required to send or receive emails. Returns an array of inboxes with their IDs, email addresses, and names. List all inboxes assigned to the AgentMail API Key

list_threads

Returns a list of thread objects including subject lines and recent message previews. The agent needs an inbox_id first. List conversation threads inside an inbox

reply_to_message

The thread will be preserved. Reply to an existing email message/thread

send_message

Requires the sender inbox_id, which you can get from list_inboxes. Send a brand new email message

update_message

Update an existing message metadata (like marking it as read)

Example Prompts for AgentMail in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with AgentMail immediately.

"Create a new inbox for our support team."

"Check all my unread threads in the main inbox today."

"Reply to the client thanking them and attach the pricing PDF."

Troubleshooting AgentMail MCP Server with LlamaIndex

Common issues when connecting AgentMail to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

AgentMail + LlamaIndex FAQ

Common questions about integrating AgentMail MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query AgentMail tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.