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LoganInTX

hass-mcp-extensions

by LoganInTX

get_device_info

Retrieve device registry details including integration, manufacturer, model, and connections to identify network protocol and configuration entry for debugging or re-pairing.

Instructions

Look up device registry info for a device — integration, manufacturer, model, connections.

Useful for determining which network/protocol a device uses (Zigbee, Z-Wave, Matter, Thread, etc.) and its config entry, so you know how to re-pair or debug it.

Pass exactly one of entity_id or device_name.

Args: entity_id: Any entity belonging to the device, e.g. "binary_sensor.motion_pantry_occupancy". device_name: Friendly device name, e.g. "Motion Pantry". Matched case-insensitively against the device's name and name_by_user fields.

Returns a dict with keys: id, name, name_by_user, manufacturer, model, sw_version, hw_version, integration (config entry domain), config_entry_title, connections (e.g. Zigbee IEEE address), identifiers, and disabled_by.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_idNo
device_nameNo
Behavior4/5

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

With no annotations, the description bears full burden. It explains the tool is read-only ('look up'), describes the return structure, and parameter requirements. It does not explicitly state non-destructiveness or permission needs, but the lookup nature is clear.

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 in paragraphs: purpose, usage, parameters, return keys. Front-loaded with core info. Slightly verbose but still efficient and clear; each sentence adds value.

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?

No output schema, but description lists all 11 return keys, covering the tool's complexity. It explains parameter matching and usage context thoroughly, making it self-contained for agent decision-making.

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

Parameters5/5

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

Schema coverage is 0%, so description compensates fully. It explains entity_id with an example, and device_name with case-insensitive matching behavior. It also clarifies the exclusive-or constraint, adding substantial meaning beyond the 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 tool's purpose: 'Look up device registry info for a device,' listing specific fields (integration, manufacturer, model, connections). It distinguishes from sibling tools like get_entity or list_entities by focusing on registry information versus state.

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 explicit usage context ('useful for determining network/protocol... for re-pairing or debugging') and parameter constraint ('pass exactly one of entity_id or device_name'). However, it does not explicitly exclude using it for state retrieval or name alternative tools.

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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