Can I check if a file is publicly accessible through the agent?

Yes. The `get_object_access_control` tool allows your AI agent to retrieve the Access Control List (ACL) for any specific file, helping you audit permissions and identify public objects.

How do I protect my files from accidental deletion in a bucket?

You can use the `enable_bucket_versioning` tool to activate versioning on your bucket. This ensures that historical copies of your files are preserved, allowing you to recover from accidental overwrites or deletions.

Can I see where my data is physically stored?

Absolutely. Use the `get_bucket_datacenter_location` tool to retrieve the specific geographic region where a bucket is hosted, ensuring you meet data residency requirements.

Wasabi MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Wasabi
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Wasabi as an MCP tool provider through the 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 Wasabi. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Wasabi?"
    )
    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 Wasabi MCP Server

Connect your Wasabi Hot Cloud Storage account to any AI agent and take full control of your cloud assets through natural conversation.

LlamaIndex agents combine Wasabi tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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

Bucket Management — List all storage buckets, create new ones, or delete obsolete containers in your account
Object Discovery — Browse and list files (objects) stored within specific buckets, including sizes and last modified dates
Data Integrity — Enable and check bucket versioning to protect against accidental file overwrites or deletions
Access Control — Audit permissions and retrieve Access Control Lists (ACL) for specific files to ensure security
Data Residency — Verify the physical geographic region where your data is hosted for compliance needs
Cleanup Tasks — Identify fractured file uploads that consume storage and permanently delete obsolete assets

The Wasabi 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.

How to Connect Wasabi to LlamaIndex via MCP

Follow these steps to integrate the Wasabi 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 10 tools from Wasabi

Why Use LlamaIndex with the Wasabi MCP Server

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

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

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

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

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

Wasabi + LlamaIndex Use Cases

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

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

Data enrichment: query Wasabi 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 Wasabi for fresh data

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

Wasabi MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Wasabi to LlamaIndex via MCP:

create_storage_bucket

Provide a globally unique lower-kebab-case name. Creates a new high-availability storage bucket in the configured Wasabi region

delete_bucket_object

This action is irreversible. Permanently deletes a specific file from a bucket

delete_storage_bucket

Note: The bucket must be completely empty first. This action is irreversible. Permanently removes an empty storage bucket

enable_bucket_versioning

Activates object versioning for a bucket

get_bucket_datacenter_location

Retrieves the physical geographic region where a bucket is hosted

get_bucket_versioning_status

Checks if object versioning is enabled for a bucket

get_object_access_control

Retrieves the access control list (ACL) for a specific file

list_bucket_objects

Returns file keys, sizes, and last modified dates. Lists the files (objects) stored within a specific bucket

list_pending_multipart_uploads

Lists incomplete multipart uploads in a bucket

list_storage_buckets

Lists all Wasabi storage buckets visible to the authenticated IAM user

Example Prompts for Wasabi in LlamaIndex

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

"List all my storage buckets in Wasabi."

"What files are inside the 'backups-2026' bucket?"

"Is versioning enabled for my 'user-data-prod' bucket?"

Troubleshooting Wasabi MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Wasabi + LlamaIndex FAQ

Common questions about integrating Wasabi 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 Wasabi tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.