FutureVault MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add FutureVault as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 FutureVault. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in FutureVault?"
)
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 FutureVault MCP Server
Connect your FutureVault account to any AI agent to automate your document management and digital vault workflows through the Model Context Protocol (MCP). FutureVault is a secure, high-compliance digital vault platform designed for financial services, wealth management, and high-net-worth individuals. This MCP server enables you to manage your vault directory, retrieve document metadata, and participate in collaborative workflows directly through natural conversation.
LlamaIndex agents combine FutureVault tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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.
Key Features
- Vault Orchestration — List all accessible digital vaults and fetch detailed configuration metadata for each.
- Directory Structure — Access and navigate complex folder hierarchies within your vaults to organize sensitive data.
- Document Oversight — List documents within specific folders and retrieve complete metadata (owner, tags, status) for individual files.
- Team & Member Management — Access vault membership lists and detailed profile information for all users with access.
- Role Discovery — List all system roles and permissions to audit your security and access control model.
- Powerful Search — Execute global searches across all vaults for specific documents or folders by name or keyword.
- Real-time Synchronization — Keep your fiduciary document repository accessible to your AI assistant without leaving your primary workspace.
The FutureVault MCP Server exposes 12 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 FutureVault to LlamaIndex via MCP
Follow these steps to integrate the FutureVault 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 12 tools from FutureVault
Why Use LlamaIndex with the FutureVault MCP Server
LlamaIndex provides unique advantages when paired with FutureVault through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine FutureVault tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain FutureVault tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query FutureVault, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what FutureVault tools were called, what data was returned, and how it influenced the final answer
FutureVault + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the FutureVault MCP Server delivers measurable value.
Hybrid search: combine FutureVault real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query FutureVault 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 FutureVault for fresh data
Analytical workflows: chain FutureVault queries with LlamaIndex's data connectors to build multi-source analytical reports
FutureVault MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect FutureVault to LlamaIndex via MCP:
get_document_metadata
Get document details
get_folder_details
Get folder metadata
get_member_details
Get member metadata
get_my_identity
Get current user profile
get_vault_details
Get vault metadata
list_digital_vaults
List all accessible vaults
list_folder_documents
List documents in folder
list_system_roles
List accessible roles
list_vault_folders
List folders in a vault
list_vault_members
List vault members
search_vault_content
Search documents/folders
verify_api_connection
Check connection
Example Prompts for FutureVault in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with FutureVault immediately.
"List all digital vaults I have access to."
"Search for 'Tax Statement' in all my vaults."
"Who are the members of the 'Acme Corporate' vault?"
Troubleshooting FutureVault MCP Server with LlamaIndex
Common issues when connecting FutureVault to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFutureVault + LlamaIndex FAQ
Common questions about integrating FutureVault 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 FutureVault 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 FutureVault to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.