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cobanov

teslamate-mcp

get_software_update_history

Retrieve firmware update history for Tesla vehicles, tracking version changes and software evolution over time.

Instructions

Get the software update history for each car. Tracks firmware updates and version changes over time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • src/tools.py:87-91 (registration)
    Defines the ToolDefinition for 'get_software_update_history', including its description and the associated SQL query file.
    ToolDefinition(
        name="get_software_update_history",
        description="Get the software update history for each car. Tracks firmware updates and version changes over time.",
        sql_file="software_update_history.sql",
    ),
  • main.py:22-28 (handler)
    Handler factory for local STDIO server that creates a function to execute the tool's SQL query synchronously.
    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:31-39 (registration)
    Registers handlers for all tools, including 'get_software_update_history', with the FastMCP server using dynamic decorator.
    # Register all tools from definitions
    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)
  • Handler for remote HTTP server that executes the predefined tool's SQL query asynchronously by looking up its definition.
    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
        )
  • Registers 'get_software_update_history' in the tools list with empty input schema (no parameters required).
    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 states the tool retrieves historical data, implying a read-only operation, but does not clarify aspects like authentication needs, rate limits, data freshness, or error handling. This is a significant gap 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and scope without any redundant information. Every sentence earns its place by adding clarity.

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 (simple data retrieval with no parameters) and the presence of an output schema (which handles return values), the description is minimally adequate. However, it lacks behavioral details (e.g., permissions, limitations) that would be helpful since no annotations are provided, making it incomplete for optimal agent use.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining what data is retrieved ('software update history for each car' and 'tracks firmware updates and version changes over time'), which provides context beyond the empty 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 with a specific verb ('Get') and resource ('software update history for each car'), and specifies what it tracks ('firmware updates and version changes over time'). However, it does not explicitly differentiate from sibling tools, which are all data retrieval tools but focus on different car metrics like charging, efficiency, or driving patterns.

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 mentions what the tool does but does not specify contexts, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone among many sibling data-fetching 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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