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 LangChain 10 tools — connect in under 2 minutes

Name: Wasabi
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Wasabi through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "wasabi": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Wasabi, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Wasabi through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Wasabi MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Wasabi via MCP

Why Use LangChain with the Wasabi MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Wasabi MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Wasabi queries for multi-turn workflows

Wasabi + LangChain Use Cases

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

RAG with live data: combine Wasabi tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Wasabi, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Wasabi tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Wasabi tool call, measure latency, and optimize your agent's performance

Wasabi MCP Tools for LangChain (10)

These 10 tools become available when you connect Wasabi to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Wasabi + LangChain FAQ

Common questions about integrating Wasabi MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.