Deleting Backblaze B2 Ghost Shards Using Claude MCP

The Problem Statement

Managing object storage at scale is rarely about the happy path. If you are running production workloads on Backblaze B2, your primary concerns aren’t just uploading files; they are the silent cost leaks and the architectural drift that happens when automation fails.

We have all dealt with it: a migration script crashes halfway through a large multipart upload, leaving behind orphaned chunks that stay in your bucket indefinitely. These “ghost” shards don’t show up in standard file listings, yet they sit there, consuming your billing quota and bloating your storage footprint. Then there is the bucket sprawl; the tendency for developers to spin up temporary buckets for testing and forget to decommission them, or even worse, leave them set to allPublic when they enough should have been allPrivate.

Manually auditing these states via the B2 CLI or web console is a reactive, manual process. It requires a human to remember to check, a human to verify, and a human to execute the cleanup. In a modern DevOps workflow, this is exactly the kind of toil we should be eliminating.

The thesis here is simple: Automating Backblaze B2 management via MCP reduces operational toil but requires strict governance over destructive tools like delete_bucket. You can gain immense speed in auditing and remediation, but you trade away the safety net of manual confirmation unless you implement proper guardrails at the gateway level.

Technical Evidence

The power of the Backblaze B2 MCP server lies in its ability to expose granular, low-level B2 primitives to your AI agent. Instead of writing custom Python scripts to crawl buckets, you can simply ask Claude or Cursor to audit your environment.

We recently encountered a scenario where a failed database dump migration left approximately 42GB of orphaned multipart upload chunks sitting in our db-dumps bucket. These were invisible to standard list_file_names calls because the uploads never officially “completed.”

By using the Backblaze B2 MCP server via Vinkius, we could use an AI agent to identify and remediate these specific cost leaks without writing a single line of new infrastructure code.

Here is how the agent performed the audit:

  # First, the agent identifies the active buckets
  # Tool call: list_buckets()
  # Output: 
  # [
  #   {"name": "production-assets", "type": "allPublic"},
  #   {"name": "db-dumps", "type": "allPrivate"},
  #   {"name": "temp-testing", "type": "allPublic"}
  # ]

  # The agent then targets the problematic bucket to find stalled uploads
  # Tool call: list_unfinished_large_files(bucket_id="db-dumps")
  # Output:
  # [
  #   {"file_name": "backup_part_01.sql", "size": "21474836480", "status": "incomplete"},
  #   {"file_name": "archive_shard_99.tar", "size": "5368709120", "status": "incomplete"}
  # ]

In this trace, the agent immediately flagged the 42GB of stalled data. Because the MCP server provides direct access to list_unfinished_large_files, the agent could see exactly what was draining our budget. From there, we instructed the agent to clean up these specific shards, effectively performing a surgical strike on our storage costs.

The integration works through Vinkius Edge. You do not need to manage B2 Application Keys inside your IDE. You simply provide your credentials once in the Vinkius dashboard, and your Connection Token allows any MCP-compatible client; be it Claude Desktop, Cursor, or Windsurf; to interact with your storage architecture securely.

Honest Limitations & Tradeoffs

No automation is without risk. The most significant danger when using an AI agent to manage cloud infrastructure is the ease of executing destructive operations.

The Backblaze B2 MCP server includes tools like delete_bucket and delete_file_version. In a natural language interface, a simple instruction like “clean up all the old testing buckets” could lead to catastrophic data loss if the agent misinterprets your intent or if your permissions are too broad.

This is where the tradeoff becomes real: you gain extreme operational velocity at the cost of requiring much stricter governance.

To mitigate this, we rely on the Vinkius Security Passport. When you subscribe to the Backblaze B2 MCP server, you can see exactly which tools are “destructive” (marked with [DESTRUCTIVE]) and what permissions they require. By using Vinkius, you can apply policies that restrict an agent’s ability to execute certain commands or require manual approval for any operation involving bucket deletion.

If you do not have these guardrails in place, you are essentially giving a LLM the “delete” key to your production storage. You must treat this connection as a high-privilege identity.

Decision Framework

How should you decide when to use the Backblaze B2 MCP server versus traditional manual management? Use the following framework:

Use AI-Driven Management (via Vinkius) When:

Auditing and Observability: You need to quickly scan for orphaned files, check bucket privacy settings, or verify SHA1 integrity across a large number of objects.
Routine Maintenance: You have repeatable cleanup tasks, such as purging old file versions or managing lifecycle transitions.

Use Manual CLI/Console Intervention When:

High-Sakes Structural Changes: Any operation that involves re-architecting your primary storage hierarchy or changing fundamental access controls on mission-critical buckets.
Initial Provisioning: Setting up the foundational security and IAM structure for a new production environment should still be done through audited, version-controlled IaC (Infrastructure as Code) like Terraform.

The goal is not to replace your existing DevOps workflows, but to augment them. The Backblaze B2 MCP server allows you to move from “reactive firefighting” to “proactive orchestration,” provided you maintain the discipline of strict permission scoping via the Vinkius AI Gateway.