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stop_all_services

Stop all running services in an Ambari cluster simultaneously for maintenance, troubleshooting, or shutdown. Automates mass service shutdown and provides progress tracking.

Instructions

Stops all services in an Ambari cluster (equivalent to "Stop All" in Ambari Web UI).

[Tool Role]: Dedicated tool for bulk stopping all services in the cluster, automating mass shutdown.

[Core Functions]:

  • Stop all running services simultaneously

  • Return request information for progress tracking

  • Provide clear success or error message for LLM automation

[Required Usage Scenarios]:

  • When users request to "stop all services", "stop everything", "cluster shutdown"

  • When cluster maintenance or troubleshooting requires mass shutdown

  • When users mention mass shutdown, bulk stop, or cluster halt

Returns: Stop operation result (success: request info, failure: English error message)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries full burden and effectively discloses key behaviors: it performs a bulk stop operation (destructive/mutative), returns request information for progress tracking, and provides success/error messages in English. It could improve by mentioning permissions or side effects on dependent services.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, but the structured sections ('[Tool Role]', '[Core Functions]', '[Required Usage Scenarios]') add redundancy and length without proportional value. Some sentences (e.g., 'automating mass shutdown') repeat ideas, reducing efficiency.

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 a 0-parameter tool with an output schema (implied by 'Returns' section), the description is largely complete: it explains the action, usage context, and return values. It could better address cluster-wide implications or error handling specifics, but annotations are absent and output schema likely covers returns.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately omits parameter details, focusing on tool behavior instead. A 5 is reserved for cases where description adds value beyond a trivial schema.

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 ('stops all services') and target resource ('in an Ambari cluster'), with explicit differentiation from sibling tools like 'stop_service' (singular) and 'restart_all_services'. The analogy to Ambari Web UI's 'Stop All' further clarifies the scope.

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?

The '[Required Usage Scenarios]' section provides explicit guidance on when to use this tool (e.g., 'cluster shutdown', 'mass shutdown', 'bulk stop'), including user request patterns and maintenance contexts. It implicitly distinguishes from 'stop_service' for individual operations.

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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