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ailumia

Amazon SP-API MCP

by ailumia

Read an SP-API artifact

get_artifact
Read-onlyIdempotent

Read a specific chunk from a large or binary result saved by Amazon SP-API operations, using id, offset, and length to access manageable data segments.

Instructions

Read a chunk from a large or binary result saved by invoke_operation or run_report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
lengthNo
offsetNo
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds value by disclosing that this reads a 'chunk' from 'large or binary' artifacts, which is a key behavioral trait beyond the annotations. However, it does not describe error behavior, chunk format (raw bytes, encoding), or how to iterate through the entire artifact. No contradiction with annotations.

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?

The description is a single, front-loaded sentence of 17 words that efficiently conveys verb, object, and provenance. It contains no filler or redundant information, every word earns its place.

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

Completeness3/5

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

The core idea and source of artifacts are clear, but for a chunked-read tool with no output schema, the description omits critical details like how output is represented (raw binary, base64, etc.), how to know total artifact size, and behavior when offset exceeds bounds. This leaves significant gaps in the agent's understanding of the tool's full behavior.

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

Parameters2/5

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

With 0% schema description coverage, the description must compensate. It mentions 'chunk', which implies offset/length usage, and indicates id refers to artifacts saved by invoke_operation/run_report. However, it does not explain the units of length (bytes vs. characters), semantics of offset, or how the id relates to an artifact. The parameters are self-named but under-described, leaving the agent to guess.

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 clearly states the action 'Read a chunk' and the resource 'a large or binary result saved by invoke_operation or run_report'. This specific verb+resource pairing distinguishes it from sibling tools that create or execute operations, making the purpose unmistakable.

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

Usage Guidelines4/5

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

The description implies use after invoke_operation or run_report, since artifacts are saved by those tools. It frames this tool as the way to access partial results, providing clear contextual guidance. However, it does not explicitly name alternatives or state when not to use it, so it lacks the exclusionary guidance seen in the highest examples.

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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