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dhevenb

Spec3 MCP Server

by dhevenb

get_car_context

Retrieve configuration, build status, and modification details for a 1994 BMW E36 325is Spec3 race car to support personalized advice, troubleshooting, or modification planning.

Instructions

Get information about the user's 1994 BMW E36 325is Spec3 race car build.

Returns current configuration, build status, modifications, and car-specific details. Call this tool when providing personalized advice, troubleshooting, or planning modifications.

Returns: dict: Car configuration, history, and current state

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function decorated with @mcp.tool(), including docstring schema, logging, and return of car context data.
    @mcp.tool()
    async def get_car_context() -> dict[str, Any]:
        """
        Get information about the user's 1994 BMW E36 325is Spec3 race car build.
    
        Returns current configuration, build status, modifications, and car-specific
        details. Call this tool when providing personalized advice, troubleshooting,
        or planning modifications.
    
        Returns:
            dict: Car configuration, history, and current state
        """
        logger.info("get_car_context called")
    
        return {
            "context": CAR_CONTEXT,
            "last_updated": "2025-10-05"
        }
  • Constant string containing the car-specific context data referenced and returned by the get_car_context tool.
    CAR_CONTEXT = """
    # My Spec3 E36 Build
    
    ## Vehicle Information
    - Year/Model: 1994 BMW E36 325is
    - Build Status: [In-progress]
    """
  • Docstring providing the tool description and return type schema used by FastMCP for tool registration.
    """
    Get information about the user's 1994 BMW E36 325is Spec3 race car build.
    
    Returns current configuration, build status, modifications, and car-specific
    details. Call this tool when providing personalized advice, troubleshooting,
    or planning modifications.
    
    Returns:
        dict: Car configuration, history, and current state
    """
  • @mcp.tool() decorator that registers the get_car_context function as an MCP tool.
    @mcp.tool()
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 states what the tool returns (configuration, build status, modifications, car-specific details) and implies it's a read-only operation by using 'Get information', but does not cover aspects like error conditions, authentication needs, or rate limits. This is adequate but has gaps for a tool with zero 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 well-structured and concise, with three sentences that each serve a distinct purpose: stating the tool's purpose, providing usage guidelines, and describing the return value. There is no wasted text, and information is front-loaded effectively.

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 complexity (0 parameters, no annotations, but an output schema exists), the description is mostly complete. It explains the purpose, usage, and return content. Since an output schema is present, the description does not need to detail return values, but it could benefit from more behavioral context (e.g., error handling).

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 appropriately does not discuss parameters, earning a high baseline score. A 5 is reserved for cases where the description adds value beyond a perfect schema, which isn't applicable here.

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

Purpose5/5

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

The description clearly states the specific action ('Get information') and the precise resource ('user's 1994 BMW E36 325is Spec3 race car build'), distinguishing it from sibling tools like get_document and list_documents which appear to handle generic documents rather than this specific car context.

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 description provides explicit guidance on when to use this tool ('when providing personalized advice, troubleshooting, or planning modifications'), offering clear context. However, it does not specify when NOT to use it or explicitly mention alternatives, which prevents a perfect score.

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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