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Check API liveness

history_get_system_info
Read-onlyIdempotent

Confirm Home Assistant's HTTP API is alive by fetching its raw liveness message. Use for basic API responsiveness checks, not for system details.

Instructions

Get Home Assistant's raw API liveness message.

Calls GET /api/, which returns only a fixed liveness message (e.g. {"message": "API running."}) — no version or system data despite the tool's name.

Use when: a plain proof that the HTTP API is answering is enough. Not for: version/location/units/components — use history_get_ha_config; for subsystem health — use system_get_system_health; for Core/OS details — use supervisor_get_core_info or supervisor_get_host_info; for a simple reachable/unreachable boolean — use system_ping_ha. Returns: {"message": "API running."} (HA's fixed liveness payload). Errors: raises httpx.HTTPStatusError if the HA API request fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, non-destructive, and closed-world behavior, so the bar is lower. The description nonetheless discloses the exact endpoint called, the fixed return payload, and the specific error raised (httpx.HTTPStatusError), which adds real behavioral context beyond the annotations.

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?

The description is well-organized with a purpose line, endpoint detail, explicit Use/Not-for routing, and Returns/Errors sections. It is somewhat long for a zero-parameter liveness check, but every section carries useful information.

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?

For a zero-parameter, read-only liveness tool, the description covers purpose, the exact endpoint, the return shape (noting the output schema notwithstanding), error behavior, and sibling disambiguation. Nothing an agent needs to call it correctly is missing.

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 tool takes zero parameters, which is the baseline-4 case. The description correctly implies no input is needed and focuses instead on the endpoint and return payload.

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 states a specific verb and resource ('Get Home Assistant's raw API liveness message') and immediately clarifies the scope, noting that despite the tool's name it returns only a fixed liveness message, not version or system data. It explicitly distinguishes itself from siblings like history_get_ha_config, system_get_system_health, supervisor_get_core_info, and system_ping_ha.

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?

It provides explicit 'Use when' and 'Not for' guidance, naming four alternative tools with the specific conditions that select them. The routing is unusually thorough and leaves little to inference.

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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