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mirastacklabs-ai

MIRASTACK Redfish MCP Server

Official

Get chassis thermal data

get_thermal
Read-onlyIdempotent

Retrieve temperature, fan, and cooling telemetry from a Redfish chassis, including inlet/exhaust temperatures and fan speeds.

Instructions

Fetch ThermalSubsystem/Thermal for temperature, fan, and cooling telemetry. Prefer this tool for inlet/exhaust temperature and fan speed requests. Returns: Object with thermal_uri plus full thermal resource payload. Example: get_thermal(chassis_uri='/redfish/v1/Chassis/1')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endpointNoConfigured endpoint name. Omit to use the default endpoint.
chassis_uriNoTarget chassis URI. Omit to auto-select the first chassis.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by disclosing the return format ('Object with thermal_uri plus full thermal resource payload') and providing a concrete example call, which helps predict behavior.

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?

Three sentences plus an example, no fluff. The first sentence states the core function, the second gives usage preference, and the third describes return payload. Very efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple: read-only, two optional params, has output schema. The description covers what it does, when to use it, and what it returns. Annotations cover safety. No missing essential information for an agent to select and invoke correctly.

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

Parameters3/5

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

The input schema covers both parameters with descriptions (endpoint and chassis_uri, both with default behavior). The description's example clarifies usage of chassis_uri but does not add semantic meaning beyond the schema's descriptions. Since schema coverage is 100%, a baseline of 3 applies.

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 uses a specific verb ('Fetch') and resource ('ThermalSubsystem/Thermal') and clearly lists what telemetry it covers (temperature, fan, cooling). It also distinguishes itself by stating a preference for this tool for inlet/exhaust temperature and fan speed requests, differentiating from sibling tools like get_power or get_sensors.

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 states 'Prefer this tool for inlet/exhaust temperature and fan speed requests,' giving explicit when-to-use guidance. However, it does not name alternative tools or explicitly state when not to use it, so it stops short of full exclusions.

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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