Home Assistant MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap. control_device handles basic on/off states, search_entities finds devices by description, and set_device_color manages color/brightness for lights. An agent can easily distinguish when to use each tool based on the specific operation needed.
Naming Consistency5/5All three tools follow a consistent verb_noun pattern with snake_case throughout: control_device, search_entities, and set_device_color. The naming is predictable and follows the same grammatical structure, making the tool set easy to understand at a glance.
Tool Count3/5With only 3 tools, this feels thin for a Home Assistant integration that presumably manages many device types and operations. While the tools cover basic control, search, and color settings, the scope suggests more operations would be needed for comprehensive home automation coverage. The count is borderline minimal for the domain.
Completeness2/5There are significant gaps in the tool surface for home automation. Missing operations include getting device status/state, adjusting non-color attributes (like temperature for thermostats or speed for fans), managing scenes/automations, and handling other entity types beyond lights. The current tools provide only partial coverage of the Home Assistant domain.
Average 3.7/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('turning it on or off') but lacks critical details: it doesn't mention permissions required, whether this is a destructive operation (e.g., if turning off a device has irreversible effects), rate limits, error handling, or what happens upon success/failure. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, and the 'Args' section efficiently documents parameters without unnecessary details. Every sentence earns its place, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, mutation operation, no output schema), the description is partially complete. It covers the basic purpose and parameter semantics but lacks behavioral details (e.g., side effects, permissions) and usage guidelines. Without annotations or output schema, it leaves gaps that could hinder an agent's ability to use the tool effectively in varied contexts.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'entity_id' is a Home Assistant entity ID with a specific format ('domain.entity') and that 'state' accepts 'on' or 'off' values. This clarifies the purpose and constraints of both parameters, compensating well for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Control a Home Assistant entity by turning it on or off.' This specifies the verb ('control'), resource ('Home Assistant entity'), and action ('turning it on or off'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'set_device_color' (which might control color instead of on/off state), so it misses the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search_entities' (which might be for finding entities) or 'set_device_color' (which might control color settings), nor does it specify prerequisites, exclusions, or contextual cues for usage. This leaves the agent with minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a write operation ('Set') but doesn't mention permissions, side effects, error conditions, or response format. While it specifies the action, it lacks critical behavioral details like whether this requires authentication, what happens on invalid inputs, or if changes are reversible.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by a structured parameter list. Every sentence earns its place by clarifying parameter details without redundancy. It's appropriately sized for a tool with multiple parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description covers the action and parameters well but lacks behavioral context (e.g., error handling, permissions) and return values. For a write operation with 5 parameters, this is minimally adequate but leaves gaps in understanding full tool behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It provides clear semantics for all 5 parameters: entity_id format ('light.entity'), RGB component ranges (0-255), and brightness as optional with range. This adds essential meaning beyond the bare schema, which only shows types and titles without context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Set the color and optionally brightness') and target resource ('a light entity'), distinguishing it from sibling tools like 'control_device' (more generic) and 'search_entities' (read-only). The verb 'set' is precise and indicates a write operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for controlling light color/brightness but doesn't explicitly state when to use this tool versus 'control_device' (which might handle other device types or operations) or 'search_entities' (for discovery). No explicit alternatives, prerequisites, or exclusions are provided, leaving usage context somewhat ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the search operation and return format (list of entity IDs with friendly names or error message), which adds useful context. However, it lacks details on permissions, rate limits, or error conditions, leaving some behavioral aspects unspecified for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with a clear purpose statement followed by structured sections for arguments and returns. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, parameter semantics, and return values adequately. However, it could benefit from more behavioral details (e.g., search scope, limitations) to fully compensate for the lack of annotations and output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% coverage. It explains the 'description' parameter as a natural language description with examples ('office light', 'kitchen fan'), clarifying its purpose and format. This compensates well for the schema's lack of documentation, though it doesn't cover all possible edge cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Search') and resource ('Home Assistant entities'), and distinguishes it from siblings by focusing on search functionality rather than control or configuration. It specifies the search is based on natural language descriptions, which is a distinct operation from the sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (searching for entities by natural language description), but does not explicitly mention when not to use it or name alternatives. It implies usage for discovery purposes, which is helpful but lacks explicit exclusions or comparisons to sibling tools like control_device or set_device_color.
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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