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get_host_details

Retrieve detailed information for a specific host or all hosts in an Ambari cluster, including hardware specs, metrics, state, and assigned components.

Instructions

Retrieves detailed information for a specific host or all hosts in the Ambari cluster.

[Tool Role]: Dedicated tool for retrieving comprehensive host details including metrics, hardware info, and components.

[Core Functions]:

  • If host_name provided: Query specific host information

  • If host_name not provided: Query all hosts and their detailed information

  • Return host hardware specs, state, metrics, and assigned components

  • Provide formatted output for LLM automation and cluster management

[Required Usage Scenarios]:

  • When users request specific host details or host status

  • When users request all hosts details or cluster-wide host information

  • When auditing or monitoring individual or all cluster nodes

  • When troubleshooting host-specific issues

Args: host_name: Name of the specific host to retrieve details for (optional, e.g., "bigtop-hostname0.demo.local")

Returns: Detailed host information (success: formatted details, failure: error message)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
host_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so the description carries the full burden. It discloses that the tool returns hardware specs, state, metrics, components, and formatted output. It does not explicitly state read-only nature or authentication needs, but the behavior is well enough described for most cases.

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

Conciseness4/5

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

Well-structured with sections like Tool Role, Core Functions, Required Usage Scenarios. However, some redundancy exists (e.g., Core Functions repeats purpose). The description is front-loaded with the primary action.

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?

Covers both modes (with and without host_name). Mentions return contents (hardware, metrics, state, components). An output schema exists so precise fields are not needed. For a single-parameter tool, this is sufficiently complete.

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 coverage is 0% (no description on host_name param). The description adds significant value by explaining host_name is optional with an example value, and clarifies its role in filtering. This compensates for the missing schema description.

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?

Fully stated: 'Retrieves detailed information for a specific host or all hosts in the Ambari cluster.' Uses a specific verb and resource, and distinguishes from siblings like list_hosts (which likely only lists names) by specifying scope and detail level.

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?

Provides clear scenarios in 'Required Usage Scenarios' (e.g., when users request host details, auditing). Lacks explicit when-not-to-use or alternative sibling recommendations, but the context is clear enough for an agent to infer appropriate use.

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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