Backblaze B2 MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Backblaze B2 as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Backblaze B2. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Backblaze B2?"
)
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 Backblaze B2 MCP Server
Connect your Backblaze B2 account to any AI agent and manage your cloud storage architecture directly through natural conversation.
LlamaIndex agents combine Backblaze B2 tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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.
O que você pode fazer
- Buckets — Create, delete, update privacy (allPrivate/allPublic), and list globally mapped storage buckets
- File Management — Hide files (soft delete), remove explicit file versions (hard delete), and list surface names
- Health Validation — List unfinished large file chunks to find failed multipart uploads
- File Intelligence — Retrieve granular file details, sizes, hashes (SHA1), and content types natively
Como funciona
1. Subscribe to this server
2. Enter your Backblaze B2 Application Key ID and Key
3. Start architecting storage and debugging files from Claude, Cursor, or any MCP-compatible client
Your AI agent now has the power to orchestrate secure and cost-effective object storage without leaving your editor.
Para quem é?
- DevOps Engineers — rapidly verify bucket states, clean up abandoned file versions, and debug failed multipart streams
- Backend Developers — create public and private storage domains directly from development spaces
- System Administrators — audit object footprint securely and perform granular file integrity checks
The Backblaze B2 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 Backblaze B2 to LlamaIndex via MCP
Follow these steps to integrate the Backblaze B2 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 Backblaze B2
Why Use LlamaIndex with the Backblaze B2 MCP Server
LlamaIndex provides unique advantages when paired with Backblaze B2 through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Backblaze B2 tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Backblaze B2 tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Backblaze B2, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Backblaze B2 tools were called, what data was returned, and how it influenced the final answer
Backblaze B2 + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Backblaze B2 MCP Server delivers measurable value.
Hybrid search: combine Backblaze B2 real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Backblaze B2 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 Backblaze B2 for fresh data
Analytical workflows: chain Backblaze B2 queries with LlamaIndex's data connectors to build multi-source analytical reports
Backblaze B2 MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Backblaze B2 to LlamaIndex via MCP:
authorize_account
Uncovers the dynamically assigned apiUrl (e.g. api003.backblazeb2.com) natively orchestrating regional data ingress specifically assigned to your billing account. Verify and extract Backblaze global session routing endpoints
create_bucket
Bootstraps essential structures before assigning automated backup syncs overriding file lock retention bounds. Provision a fresh logical Storage Bucket partition
delete_bucket
Fails intentionally via 400 Bad Request if standard files or hidden object versions persist nested inside. Ensure all lifecycle rules completed purging prior to command. Irreversibly delete an entirely empty Backblaze Storage Bucket
delete_file_version
Instantly removes the storage footprint avoiding long-term retention hoarding. Irreversibly vaporize specific absolute chunk data from disk arrays
get_file_info
Obtain granular checksum and headers associated with one precise B2 File
hide_file
Does not actually delete binary payload, merely injects a null-marker shadowing the actual file ensuring b2_list_file_names ignores it, enforcing safe soft deletion logic. Mark an active B2 file as hidden leaving data for lifecycle sweep
list_buckets
Crucial to resolve immutable String Bucket IDs prerequisite to executing downstream CRUD operations strictly inside native borders. Retrieve the exact Storage Buckets existing globally on the B2 Account
list_file_names
Examines precise .pdf, .mp4 file locations avoiding hidden shadowed states produced by lifecycle overwrite semantics. Paginate primary surface-level object metadata from a specific Bucket
list_unfinished_large_files
Crucial for verifying broken API pipelines originating from external S3 clients failing CompleteMultipartUpload. Scan B2 nodes for stalled Multipart Upload chunk aggregates
update_bucket
Use only for static asset endpoints. Mutate global ACL privacy settings for a bounded Bucket
Example Prompts for Backblaze B2 in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Backblaze B2 immediately.
"What buckets do we have enabled in Backblaze B2 right now?"
"Create a new private bucket named 'ai-crawler-logs-2026' and make it private."
"Get the file details and SHA1 for the object 'index.html' in the public bucket."
Troubleshooting Backblaze B2 MCP Server with LlamaIndex
Common issues when connecting Backblaze B2 to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBackblaze B2 + LlamaIndex FAQ
Common questions about integrating Backblaze B2 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 Backblaze B2 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 Backblaze B2 to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.