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cloin

SemaphoreUI MCP Server

by cloin

analyze_task_failure

Analyze a failed Ansible task to retrieve detailed failure context, including task specifics, template data, and outputs, for AI-assisted debugging.

Instructions

Analyze a failed task for LLM processing, gathering comprehensive failure context.

Args: project_id: ID of the project task_id: ID of the task to analyze

Returns: Comprehensive failure analysis data including task details, template context, and outputs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
task_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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 the tool as analyzing and gathering context, which implies a read-only operation, but does not explicitly disclose side effects, authentication needs, or rate limits. The description is adequate but not thorough.

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 sentence followed by a structured Args section. It is concise, front-loaded with the core purpose, and contains no extraneous 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 the existence of an output schema, the description provides a high-level overview of what is returned (task details, template context, outputs). It is complete enough for a failure analysis tool, but lacks mentions of prerequisites or error conditions.

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

Parameters4/5

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

Schema description coverage is 0%, so the description compensates by listing both parameters (project_id, task_id) with brief explanations. This adds value beyond the schema's bare types and titles, though more detail on formatting or constraints would improve it.

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 verb 'Analyze' and resource 'failed task', specifying it gathers comprehensive failure context. This distinguishes it from siblings like 'get_task' or 'get_task_raw_output' which provide basic task info or raw output, not failure analysis.

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 usage when a task has failed and needs LLM processing, but it does not explicitly state when not to use it or mention alternatives such as 'get_task' or 'filter_tasks'. This leaves room for ambiguity.

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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