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@yawlabs/aws-mcp

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by YawLabs

aws_resource_get

Read-onlyIdempotent

Read a single AWS resource by its CloudFormation type name and primary identifier, using the Cloud Control API. Supports hundreds of resource types like Lambda functions, S3 buckets, and IAM roles.

Instructions

Read a single AWS resource via Cloud Control API. Covers hundreds of resource types with a CloudFormation schema. typeName is '::::' (e.g. 'AWS::Lambda::Function'); identifier is the primary key for that type (function name, bucket name, IAM role name, ARN, or composite id). Returns parsed Properties. For resources not covered by CCAPI or for data-plane operations, use aws_call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNameYesCloudFormation type name, e.g. 'AWS::Lambda::Function', 'AWS::S3::Bucket', 'AWS::IAM::Role'.
identifierYesPrimary identifier for the resource (function name, bucket name, ARN, or composite id).
profileNoOverride session profile for this call.
regionNoOverride session region for this call.
timeoutMsNoTimeout in milliseconds. Default 60000.
Behavior4/5

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

Annotations already provide readOnlyHint, destructiveHint, and idempotentHint. The description adds that it uses Cloud Control API and returns parsed Properties, which is useful context. No contradictions 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 concise with 5 sentences, front-loading the main purpose. Every sentence adds value: action, coverage, parameter explanation, output hint, and usage alternative. No redundant information.

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

Completeness4/5

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

Given there is no output schema, the description does mention 'Returns parsed Properties', though it could be more specific. It covers the essential usage context and alternative tool. Adequate for a read operation with good annotations.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant meaning: explains typeName format with examples, identifier types (function name, bucket name, ARN, composite id), and timeout default. This goes beyond the schema descriptions.

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 it reads a single AWS resource via Cloud Control API, covers hundreds of resource types, and provides the naming convention for typeName. It effectively distinguishes itself from sibling tools like aws_call by specifying when to use alternative.

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 this tool (for resources covered by CCAPI) and when not (use aws_call for non-covered resources or data-plane operations). Also explains the format of typeName and examples of identifiers.

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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