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cobanov

teslamate-mcp

get_battery_degradation_over_time

Track battery health metrics and capacity changes over time to monitor degradation for Tesla vehicles.

Instructions

Get the battery degradation over time for each car. Tracks battery health metrics and capacity changes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:22-28 (handler)
    Factory that creates the synchronous handler function for executing the tool's SQL query.
    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:32-38 (registration)
    Dynamically creates and registers the MCP tool handler for get_battery_degradation_over_time (and all others) with FastMCP using stdio transport.
    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 definition specifying name, description, and SQL file for the tool.
    ToolDefinition(
        name="get_battery_degradation_over_time",
        description="Get the battery degradation over time for each car. Tracks battery health metrics and capacity changes.",
        sql_file="battery_degradation_over_time.sql",
    ),
  • Asynchronous handler function that executes the predefined tool's SQL query using the tool name.
    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_remote.py:179-186 (registration)
    Registers the tool schema (name, description, empty input schema) in the list_tools MCP handler for HTTP transport.
    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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions tracking battery health metrics and capacity changes, which implies a read-only operation, but doesn't specify data freshness, time range defaults, aggregation methods, or output format. This leaves significant behavioral gaps for a tool that likely returns time-series data.

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 appropriately concise with two clear sentences that directly address the tool's purpose. It avoids unnecessary elaboration while maintaining readability, though it could be slightly more front-loaded by combining concepts more efficiently.

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 (tracking degradation over time), the presence of an output schema helps, but the description lacks sufficient context about what 'over time' means (e.g., daily, monthly, all-time), how data is aggregated, or what specific metrics are included. With no annotations and minimal behavioral details, it's adequate but has clear gaps.

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 zero parameters with 100% schema description coverage, so the schema fully documents the input requirements. The description doesn't need to explain parameters, and it appropriately avoids redundant information, earning a baseline score of 4 for parameter-free tools.

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 specific verbs ('get', 'tracks') and resources ('battery degradation over time', 'battery health metrics', 'capacity changes'). It distinguishes from some siblings like 'get_battery_health_summary' by focusing on temporal trends rather than summary statistics, though it doesn't explicitly differentiate from all potential alternatives.

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. It doesn't mention prerequisites, timing considerations, or compare it to siblings like 'get_battery_health_summary' or 'get_daily_battery_usage_patterns', leaving the agent to infer usage context independently.

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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