How to Use the Wasabi MCP in LlamaIndex

Q: How can I use Wasabi and LlamaIndex together?

You run a tool like liststoragebuckets via your agent. The resulting list of bucket names is then indexed into the vector store, allowing subsequent queries to reference that specific infrastructure data.

Q: Does Wasabi and LlamaIndex handle deletion records?

Yes. Running deletebucketobject provides a loggable event. This information can be indexed so you can ask, 'When was file X last deleted?' and get an answer grounded in the API interaction.

Q: How do I check object permissions with Wasabi and LlamaIndex?

You call getobjectaccesscontrol. The resulting ACL is then part of your knowledge base. This lets you ask questions like, 'Which files are public?' and get a precise answer from the stored data.

Q: If I query Wasabi and LlamaIndex about bucket locations?

You use getbucketdatacenterlocation. The returned physical region information is indexed. This lets you build RAG applications that answer, 'Where are all my European data stored?' by checking the index.

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Connect Wasabi MCP to LlamaIndex

Create your Vinkius account to connect Wasabi to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Wasabi with LlamaIndex

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Search Infrastructure State

LlamaIndex indexes the results from infrastructure tools, making them searchable. You can run `list_storage_buckets` once, and then query your knowledge base later to ask: 'Which bucket holds all my user logs?' The answer is grounded in the API data. This lets you combine live cloud state with documents, creating a unified index for semantic search.

Track Object Changes

Need to know what files were recently updated? Use `list_bucket_objects` to retrieve file keys and modification dates. The output of this tool becomes searchable knowledge. You can query your index asking for 'all objects modified last week,' getting answers based on actual API records. This mechanism moves beyond simple retrieval; you're querying the history of your data infrastructure.

Manage Bucket Configuration

The MCP Server allows you to read key configuration details. For instance, running `get_bucket_versioning_status` gives a definitive status report. LlamaIndex indexes this output so that any future query about versioning simply returns the documented state. This ensures your knowledge base reflects not just documents, but also current operational parameters of your cloud resources.

Setup guide

Set up Wasabi MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Wasabi MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Wasabi tools.",
)
response = await agent.run("List recent Wasabi data")

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Common questions about Wasabi MCP in LlamaIndex

The server touches object metadata, including file keys, sizes, and last modified dates. These specific fields are indexed into the vector store, allowing you to query them semantically.

You run a tool like `list_storage_buckets` via your agent. The resulting list of bucket names is then indexed into the vector store, allowing subsequent queries to reference that specific infrastructure data.

Yes. Running `delete_bucket_object` provides a loggable event. This information can be indexed so you can ask, 'When was file X last deleted?' and get an answer grounded in the API interaction.

You call `get_object_access_control`. The resulting ACL is then part of your knowledge base. This lets you ask questions like, 'Which files are public?' and get a precise answer from the stored data.

You use `get_bucket_datacenter_location`. The returned physical region information is indexed. This lets you build RAG applications that answer, 'Where are all my European data stored?' by checking the index.

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