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backblaze-labs

Backblaze B2 MCP Server

Official

s3_get_object

Retrieve an object from B2: read small files inline as base64 or stream any size to a local path using saveToPath.

Instructions

Read a SMALL object inline (≤1 MiB, returned base64) — for manifests, sidecars, and configs the agent must inspect — or stream any size to a local path with saveToPath. saveToPath writes the fetched bytes to the local filesystem (creating parent directories), removes the partial file if the stream fails (cleanup of its own output only), and performs no mutation of B2 or any remote data. For real object data, generate a GetObject URL with s3_get_presigned_url and download directly from B2 (bytes never pass through the server or the model context).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesThe object key.
rangeNoByte range, e.g. 'bytes=0-1048575'.
bucketYesThe bucket name.
versionIdNoSpecific version of the object to retrieve.
saveToPathNoIf provided, save the file to this local path.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds meaningful behavioral detail beyond the annotations: inline returns base64, saveToPath creates parent directories, partial files are removed on stream failure, and no remote B2 mutation occurs. This is especially valuable because readOnlyHint=false could otherwise leave an agent uncertain about side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense, purposeful sentences with no filler. The core usage choice is front-loaded, followed by side-effect disclaimers and the alternative routing. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, but the description explains the return format for inline reads and the local-path behavior for saveToPath. Combined with full schema coverage for parameters, an agent has everything needed to invoke it correctly, including edge-case cleanup and the alternative for large real data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers all 5 parameters at 100%, so the baseline is already strong. The description goes further by explaining the practical semantics of saveToPath (local write, parent directory creation, cleanup) and the inline/base64 return behavior, which the schema alone does not convey.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource: 'Read a SMALL object inline' or stream to a local path, with named use cases (manifests, sidecars, configs). It also distinguishes itself from s3_get_presigned_url, so a selecting agent can tell them apart immediately.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use inline reads (small objects ≤1 MiB to inspect), when to use saveToPath (any size), and when to use the alternative s3_get_presigned_url for real object data. This gives clear selection criteria without relying on inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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