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ginkida

portainer-mcp

by ginkida

portainer_laravel_errors

Get Laravel application error details from container logs to diagnose HTTP 500 errors after identifying them in nginx access logs.

Instructions

Get Laravel application-level errors from storage/logs/laravel.log.

Executes inside each running backend/horizon container of a stack to read the actual Laravel error log (not nginx access log). Returns production.ERROR entries with exception messages and context.

Use this AFTER portainer_stack_logs_errors to get root cause details behind HTTP 500 errors seen in nginx access logs.

Args: stack_name: Stack name prefix (e.g. "taylor", "blog", "somnlyx") tail: Number of error lines to return per container (default 50, max 200) endpoint_id: Target endpoint ID (uses default if omitted)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stack_nameYes
tailNo
endpoint_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It describes execution inside containers, read-only nature (reading log file), and return of error entries. However, it could be more transparent about edge cases like missing logs or permissions.

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 (8 lines) with clear sections: purpose, execution context, usage guideline, and parameter documentation. Every sentence adds value without redundancy.

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 purpose, usage, parameters, and behavior adequately for a tool with 3 parameters. However, it lacks detail on output format despite having an output schema; mentioning 'Returns production.ERROR entries with exception messages and context' is somewhat vague.

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 0%, but the description explains each parameter with examples and constraints (e.g., tail default 50, max 200; endpoint_id uses default if omitted). This adds significant meaning beyond the schema's type/default annotations.

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 gets Laravel application-level errors from a specific log file, distinguishing it from nginx access logs. It also differentiates itself from siblings by focusing on production.ERROR entries and specifying a sequential use case after portainer_stack_logs_errors.

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 'Use this AFTER portainer_stack_logs_errors to get root cause details behind HTTP 500 errors seen in nginx access logs', providing a clear when-to-use and relationship with a sibling tool.

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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