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ginkida

portainer-mcp

by ginkida

portainer_laravel_tinker

Run PHP code inside a Laravel application's container using Artisan Tinker. Inspect database records, model state, or apply fixes without restarting services.

Instructions

Execute PHP code via Laravel Tinker inside a stack's backend container.

Finds a running backend container for the given stack and runs php artisan tinker --execute="<code>". Useful for inspecting database records, checking model state, running one-off fixes, and debugging application issues.

Args: stack_name: Stack name prefix (e.g. "taylor", "blog", "somnlyx") code: PHP code to execute (will be passed to tinker --execute) endpoint_id: Target endpoint ID (uses default if omitted)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stack_nameYes
codeYes
endpoint_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description explains the mechanism: finding a running container and executing `php artisan tinker --execute`. However, it lacks disclosure of potential side effects (e.g., code can modify data) and any required permissions. With no annotations, it partially informs agents but could be more explicit about risks.

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 and well-structured. The first sentence states the purpose, followed by mechanism and use cases, then a clear arg list. Every sentence is valuable and front-loaded.

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?

The description covers the tool's operation and use cases. The presence of an output schema (not shown) alleviates the need to describe return values. However, it could mention error handling or limitations for executing arbitrary code.

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?

The description adds significant meaning beyond the input schema: it defines 'stack_name' as a prefix with examples, 'code' as PHP code passed to tinker, and 'endpoint_id' as optional with default behavior. This compensates for the 0% schema description coverage.

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 specific action ('Execute PHP code via Laravel Tinker') and the target resource ('inside a stack's backend container'). It effectively distinguishes from sibling tools like portainer_container_exec by specifying it's for Laravel Tinker.

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 provides explicit use cases: 'inspecting database records, checking model state, running one-off fixes, and debugging application issues.' It gives clear context for when to use the tool, though it does not explicitly mention when not to use it or alternatives.

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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