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cobanov

teslamate-mcp

get_efficiency_by_month_and_temperature

Analyze how seasonal temperature changes affect vehicle efficiency by retrieving monthly efficiency data correlated with temperature for each car.

Instructions

Get the efficiency by month and temperature for each car. Analyzes how seasonal temperature changes affect vehicle efficiency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Core handler function for executing predefined tools like get_efficiency_by_month_and_temperature. Retrieves the tool definition and executes the associated SQL query asynchronously using the database pool.
    async def execute_predefined_tool(tool_name: str) -> List[Dict[str, Any]]:
        """Execute a predefined tool by name"""
        if not app_context:
            raise RuntimeError("Application context not initialized")
    
        tool = get_tool_by_name(tool_name)
        return await app_context.db_manager.execute_query_async(
            tool.sql_file, app_context.db_pool
        )
  • main.py:22-28 (handler)
    Factory that creates synchronous handler functions for each predefined tool by wrapping the database sync query execution for the specific SQL file.
    def create_tool_handler(sql_file: str):
        """Factory function to create tool handlers"""
    
        def handler() -> List[Dict[str, Any]]:
            return db_manager.execute_query_sync(sql_file)
    
        return handler
  • main.py:32-39 (registration)
    Registration loop that dynamically creates and registers individual handler functions for each tool, including get_efficiency_by_month_and_temperature, using FastMCP's @tool decorator.
    for tool_def in TOOL_DEFINITIONS:
        tool_func = create_tool_handler(tool_def.sql_file)
        tool_func.__doc__ = tool_def.description
        tool_func.__name__ = tool_def.name
    
        # Register the tool with the MCP server
        mcp.tool()(tool_func)
  • Tool schema definition specifying the name, description, and SQL query file for the get_efficiency_by_month_and_temperature tool.
    ToolDefinition(
        name="get_efficiency_by_month_and_temperature",
        description="Get the efficiency by month and temperature for each car. Analyzes how seasonal temperature changes affect vehicle efficiency.",
        sql_file="efficiency_by_month_and_temperature.sql",
    ),
  • main_remote.py:178-186 (registration)
    Registration of predefined tools in the list_tools() method for the remote HTTP MCP server, including the tool with empty input schema (no parameters).
    # Add all predefined tools
    for tool_def in TOOL_DEFINITIONS:
        tools.append(
            types.Tool(
                name=tool_def.name,
                description=tool_def.description,
                inputSchema={"type": "object", "properties": {}},
            )
        )
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 mentions analysis but doesn't specify whether this is a read-only operation, if it requires authentication, what data format is returned, or any performance characteristics. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 efficiently structured in two sentences: the first states the core functionality, and the second adds analytical context. Every sentence earns its place without redundancy or unnecessary elaboration, making it appropriately front-loaded and concise.

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

Completeness3/5

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

Given the tool's complexity (analysis of efficiency by month and temperature) and the presence of an output schema, the description is minimally adequate. It explains what the tool does but lacks details on behavioral traits, usage context, or output interpretation. With no annotations and rich sibling tools, it should provide more guidance to be fully complete.

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 has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't need to compensate for any parameter gaps, and it appropriately doesn't mention parameters. A baseline of 4 is applied since no parameters exist.

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 with a specific verb ('Get') and resource ('efficiency by month and temperature for each car'), and adds analytical context ('Analyzes how seasonal temperature changes affect vehicle efficiency'). However, it doesn't explicitly differentiate from siblings like 'get_average_efficiency_by_temperature' or 'get_total_distance_and_efficiency', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does 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. With multiple sibling tools related to efficiency and temperature analysis, there's no indication of specific use cases, prerequisites, or comparisons to tools like 'get_average_efficiency_by_temperature' or 'get_monthly_driving_summary'.

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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