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shamimlaravel

laravel13-livewire4.2.1-mcp

generate_job

Generate a Laravel 13.2 queued job with queue, connection, backoff attributes, enum support, and retry handling.

Instructions

Generate a Laravel 13.2 queued job with #[Queue], #[Connection], #[Backoff] attributes, enum support, and retry handling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queueNoQueue name or enum casedefault
triesNoMaximum retry attempts
uniqueNoShould job be unique
backoffNoRetry backoff delays (Laravel 13.2 variadic #[Backoff])
jobNameYesJob class name e.g. 'ProcessOrder'
timeoutNoJob timeout in seconds
connectionNoQueue connection or enum case
propertiesNo
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions generation attributes but does not describe side effects such as file creation, directory placement, overwriting behavior, or required Laravel project context. The phrase 'Generate a ... job' implies a file be created, but the absence of details about the generation process or potential prerequisites is a notable gap.

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, concise sentence that front-loads the core purpose and key distinguishing features. Every phrase earns its place—'Laravel 13.2 queued job' sets the artifact type, and the attribute list and feature mentions provide essential context without redundancy.

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

Completeness2/5

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

Despite having 8 parameters and no output schema, the description is very brief and does not explain what happens after generation (e.g., file output, location, or follow-up actions). It lacks critical context for a generator tool, such as whether it writes to a specific directory or how 'enum support' affects the generated code. The high schema coverage mitigates parameter ambiguity, but the overall description is insufficient for a tool of this complexity.

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

Parameters3/5

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

Schema description coverage is high (88%), so the schema already documents most parameters (jobName, queue, tries, etc.). The description adds context about Laravel 13.2 attributes and enum support, but does not detail specific parameter syntax or relationships beyond what the schema provides. Thus, it meets the baseline but does not significantly enhance parameter understanding.

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 tool generates a Laravel 13.2 queued job, specifying key attributes (#[Queue], #[Connection], #[Backoff]) and features like enum support and retry handling. This distinguishes it from sibling generators (e.g., generate_eloquent_model, generate_policy) by naming the exact artifact type. The verb 'Generate' and resource 'Laravel 13.2 queued job' make the purpose unambiguous.

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

Usage Guidelines3/5

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

The description implies the tool should be used when creating a queued job, but it does not explicitly state when to use it over alternatives or provide exclusions. There is no mention of when not to use this tool or which sibling tools might be more appropriate for other scenarios. However, the context is clear enough for an agent to infer the primary use case.

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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