Filemail MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Complete Transfer, Delete Transfer, Get Configuration, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Filemail 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 Filemail app connector for LlamaIndex is a standout in the Productivity 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 Filemail. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Filemail?"
)
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 Filemail MCP Server
Connect your Filemail account to any AI agent and take full control of your secure file sharing and transfer workflows through natural conversation.
LlamaIndex agents combine Filemail 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
- Transfer Orchestration — Initialize large file transfers programmatically and retrieve secure upload URLs and metadata
- Delivery Tracking — Monitor sent and received transfers in real-time, including download statuses and expiration dates
- Asset Access — Programmatically retrieve download links and compressed ZIP URLs for any transfer in your account
- Contact Management — Access your address book and manage recipient directories to streamline your sharing operations
- System Monitoring — Track account storage limits, configuration, and API connectivity directly through your agent
The Filemail 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 Filemail tools available for LlamaIndex
When LlamaIndex connects to Filemail through Vinkius, your AI agent gets direct access to every tool listed below — spanning large-file-transfer, secure-sharing, download-tracking, 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.
Finalize a file transfer
Delete a transfer
Get account configuration
Get details of a specific transfer
Get user profile
Initialize a new file transfer
List contacts
List received transfers (inbox)
List sent transfers
Login to Filemail
Connect Filemail to LlamaIndex via MCP
Follow these steps to wire Filemail 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 Filemail MCP Server
LlamaIndex provides unique advantages when paired with Filemail through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Filemail tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Filemail tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Filemail, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Filemail tools were called, what data was returned, and how it influenced the final answer
Filemail + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Filemail MCP Server delivers measurable value.
Hybrid search: combine Filemail real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Filemail 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 Filemail for fresh data
Analytical workflows: chain Filemail queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Filemail in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Filemail immediately.
"List all my sent transfers on Filemail."
"Get the download link for transfer ID 'ABC-123'."
"Check my Filemail account storage usage."
Troubleshooting Filemail MCP Server with LlamaIndex
Common issues when connecting Filemail to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFilemail + LlamaIndex FAQ
Common questions about integrating Filemail MCP Server with LlamaIndex.