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domain_summary_tool

Summarize Home Assistant entities by domain to analyze state distribution, view examples, and identify common attributes before retrieving full entity lists.

Instructions

Get a summary of entities in a specific domain

Args: domain: The domain to summarize (e.g., 'light', 'switch', 'sensor') example_limit: Maximum number of examples to include for each state

Returns: A dictionary containing: - total_count: Number of entities in the domain - state_distribution: Count of entities in each state - examples: Sample entities for each state - common_attributes: Most frequently occurring attributes

Examples: domain="light" - get light summary domain="climate", example_limit=5 - climate summary with more examples Best Practices: - Use this before retrieving all entities in a domain to understand what's available

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYes
example_limitNo

Implementation Reference

  • The main handler function for the domain_summary_tool, decorated with @mcp.tool() for registration. It logs the call and delegates to the summarize_domain helper function from app.hass.
    @mcp.tool()
    @async_handler("domain_summary")
    async def domain_summary_tool(domain: str, example_limit: int = 3) -> Dict[str, Any]:
        """
        Get a summary of entities in a specific domain
        
        Args:
            domain: The domain to summarize (e.g., 'light', 'switch', 'sensor')
            example_limit: Maximum number of examples to include for each state
        
        Returns:
            A dictionary containing:
            - total_count: Number of entities in the domain
            - state_distribution: Count of entities in each state
            - examples: Sample entities for each state
            - common_attributes: Most frequently occurring attributes
            
        Examples:
            domain="light" - get light summary
            domain="climate", example_limit=5 - climate summary with more examples
        Best Practices:
            - Use this before retrieving all entities in a domain to understand what's available    """
        logger.info(f"Getting domain summary for: {domain}")
        return await summarize_domain(domain, example_limit)
    
    @mcp.tool()
  • The core helper function that implements the domain summary logic: fetches entities, computes state distribution, examples, and common attributes.
    async def summarize_domain(domain: str, example_limit: int = 3) -> Dict[str, Any]:
        """
        Generate a summary of entities in a domain
        
        Args:
            domain: The domain to summarize (e.g., 'light', 'switch')
            example_limit: Maximum number of examples to include for each state
            
        Returns:
            Dictionary with summary information
        """
        entities = await get_entities(domain=domain)
        
        # Check if we got an error response
        if isinstance(entities, dict) and "error" in entities:
            return entities  # Just pass through the error
        
        try:
            # Initialize summary data
            total_count = len(entities)
            state_counts = {}
            state_examples = {}
            attributes_summary = {}
            
            # Process entities to build the summary
            for entity in entities:
                state = entity.get("state", "unknown")
                
                # Count states
                if state not in state_counts:
                    state_counts[state] = 0
                    state_examples[state] = []
                state_counts[state] += 1
                
                # Add examples (up to the limit)
                if len(state_examples[state]) < example_limit:
                    example = {
                        "entity_id": entity["entity_id"],
                        "friendly_name": entity.get("attributes", {}).get("friendly_name", entity["entity_id"])
                    }
                    state_examples[state].append(example)
                
                # Collect attribute keys for summary
                for attr_key in entity.get("attributes", {}):
                    if attr_key not in attributes_summary:
                        attributes_summary[attr_key] = 0
                    attributes_summary[attr_key] += 1
            
            # Create the summary
            summary = {
                "domain": domain,
                "total_count": total_count,
                "state_distribution": state_counts,
                "examples": state_examples,
                "common_attributes": sorted(
                    [(k, v) for k, v in attributes_summary.items()], 
                    key=lambda x: x[1], 
                    reverse=True
                )[:10]  # Top 10 most common attributes
            }
            
            return summary
        except Exception as e:
            return {"error": f"Error generating domain summary: {str(e)}"}
  • app/server.py:608-609 (registration)
    MCP tool registration decorators for the domain_summary_tool.
    @mcp.tool()
    @async_handler("domain_summary")
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 return structure (dictionary with specific keys) and includes examples, which adds useful context. However, it doesn't mention performance aspects like rate limits, error conditions, or whether this is a read-only operation (though implied by 'get'). The description adds value but lacks comprehensive behavioral traits.

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?

The description is well-structured with clear sections (Args, Returns, Examples, Best Practices), each serving a distinct purpose without redundancy. Every sentence earns its place by providing essential information. The front-loaded purpose statement is followed by organized details, 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.

Completeness4/5

Given 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, no output schema, no annotations), the description is quite complete. It covers purpose, parameters, return values, examples, and usage guidelines. The only gap is the lack of output schema, but the description compensates by detailing the return structure. For a summary tool, this provides sufficient context for effective use.

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?

The schema description coverage is 0%, so the description must fully compensate. It successfully does so: the 'Args' section explains both parameters with examples ('e.g., 'light', 'switch', 'sensor'' for domain and 'example_limit=5' for the limit), and the 'Examples' section provides practical usage scenarios. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Get a summary of entities in a specific domain', which is a specific verb+resource combination. It distinguishes from siblings like 'list_entities' by focusing on summarization rather than listing, though it doesn't explicitly contrast with 'search_entities_tool' or 'system_overview'. The purpose is unambiguous but could be more differentiated.

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 'Best Practices' section provides explicit guidance: 'Use this before retrieving all entities in a domain to understand what's available'. This gives clear context for when to use the tool, suggesting it's for preliminary exploration. However, it doesn't specify when NOT to use it or name alternatives among siblings, leaving some ambiguity about tool selection.

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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