IronShard Object Storage Sandbox MCP Server

PRODUCTION locked · stays clean MIRROR live sync ✕ model training discarded ✕ regression tests discarded ✓ RAG pipeline promoted ✕ fine-tuning discarded

Real dataZero production risk

A live, governed copy of production your agents can branch in seconds. Work in isolation, promote what's good, or roll back to any point in time. Train, test, and experiment freely; production stays untouched.

Zero egress by defaultTune for speed when you need it

Read-heavy agents stay on a zero-egress mix, so automated reads never run up a bill. Latency-sensitive ones optimize for speed across the providers closest to their workloads, set by their credentials.

agent-rag

batch-sync

edge-serve

$0 EGRESS

cost-safe

LOW LATENCY

geo-optimal

Each credential sets its own point on the dial. No surprise read bills, no one-size-fits-all trade-off.

Agents get accessYou keep control

The first governed S3-compatible storage for AI: every agent request authenticated, every action scoped by policy, every decision traceable via MCP.

CONNECTED

The agent is known

agent-finance-01 · connected over MCP

✓ connected

IN ITS LANE

Sees only what it should

read-only · /datasets/q1/*

✓ in scope

SERVED

Gets what it needs, fast

payroll.csv · decrypted in transit

✓ delivered

TRACEABLE

You can always see what it did

sha256:3f9a2d1c8b7e · one-click lookup

✓ logged

Every file your AI touchedSigned and on record

One line of code gives you a complete, immutable record of every file your AI touches. Signed, searchable, and ready when the audit question arrives.

ironshard.log · live feed

sha256 · event sealed 14:22:14 UTC

3f9a2d1c8b7e4f0a6c5d...

✓immutable · signed · stored

Multi-Cloud Architecture

How a single file becomes private, provider-agnostic, and always available.

Mixed for resilience·Pinned to your regions·GDPR-safe everywhere

encrypt

End-to-End Encryption

Your data is encrypted before it ever leaves your device.

fragment

Erasure-Coded Fragmentation

Files are split and erasure-coded. Each shard alone is meaningless but fully recoverable.

distribute

Multi-Cloud Distribution

Shards are stored across your chosen jurisdictions, supporting compliance and resilience.

reconstruct

Authorised Access Only

Files are decrypted only for verified users. Every reconstruction is secure and auditable.

Keep your S3 toolsChange one line

IronShard is S3-compatible: your existing SDKs, scripts, and tools work by swapping a single endpoint.

Already have data? Import it over the S3 API.

import boto3

# The only change — swap the endpoint URL
s3 = boto3.client(
    "s3",
    endpoint_url="https://s3.amazonaws.com",
)

# Everything else stays the same
s3.download_file("my-bucket", "datasets/dataset.parquet", "local.parquet")

python · boto3

Same client. New endpoint.

endpoint_url="https://s3.amazonaws.com"

keep your SDK

same API calls

Start over MCP or by hand

Let your agent set it up

Create an account, hand your agent a key, and let it provision and run storage for your workload.

1Create your account Open the console →
2Connect your agentCopy your MCP key from Settings and add it to your agent (Claude, Cursor, VS Code, or any MCP-capable client).
3PromptOnce connected, ask your agent: Connect to IronShard over MCP and provision an isolated sandbox bucket for AI experimentation, with zero-egress reads and a signed audit trail I can verify.Copy

IronShard is discoverable over MCP, so an agent can find it, evaluate it, and provision storage end to end on its own. Read the docs here.

Set it up yourself

Create a bucket in the console, generate an S3 access key, and point any S3-compatible SDK at it (AWS SDK, boto3, rclone). Your tools, your control.

Both paths share the same account and buckets: your agent works over MCP while your code uses the S3 API.

IronShard Object Storage Sandbox

Documentation