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validate_quantconnect_auth

Verify QuantConnect authentication configuration to ensure trading platform access and functionality.

Instructions

Validate current QuantConnect authentication configuration.

Returns: Dictionary containing authentication validation results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main execution handler for the 'validate_quantconnect_auth' tool. Retrieves the current authentication instance using get_auth_instance(), validates it via auth.validate_authentication(), and returns a dictionary with validation status, message, and auth details.
    @mcp.tool()
    async def validate_quantconnect_auth() -> Dict[str, Any]:
        """
        Validate current QuantConnect authentication configuration.
    
        Returns:
            Dictionary containing authentication validation results
        """
        try:
            auth = get_auth_instance()
    
            if auth is None:
                return {
                    "status": "error",
                    "error": "Authentication not configured",
                    "message": "Use configure_quantconnect_auth to set up credentials first",
                    "authenticated": False,
                }
    
            # Validate authentication
            is_valid, message = await auth.validate_authentication()
    
            return {
                "status": "success" if is_valid else "error",
                "authenticated": is_valid,
                "message": message,
                "user_id": auth.user_id,
                "organization_id": auth.organization_id,
                "has_organization": auth.organization_id is not None,
            }
    
        except Exception as e:
            return {
                "status": "error",
                "error": str(e),
                "message": "Failed to validate authentication",
                "authenticated": False,
            }
  • Registration block where register_auth_tools(mcp) is called, which defines and registers the 'validate_quantconnect_auth' tool using FastMCP's @mcp.tool() decorator.
    safe_print("🔧 Registering QuantConnect tools...")
    register_auth_tools(mcp)
    register_project_tools(mcp)
    register_file_tools(mcp)
    register_backtest_tools(mcp)
    register_live_tools(mcp)
    register_optimization_tools(mcp)
  • Alternative entrypoint registration block where register_auth_tools(mcp) is called to register the auth tools including 'validate_quantconnect_auth'.
    safe_print("🔧 Registering QuantConnect tools...")
    register_auth_tools(mcp)
    register_project_tools(mcp)
    register_file_tools(mcp)
    register_backtest_tools(mcp)
    register_live_tools(mcp)
    register_optimization_tools(mcp)
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 the tool validates authentication and returns a dictionary, but lacks details on what validation entails (e.g., checks for tokens, permissions, or connectivity), potential errors, or side effects. For a tool with zero annotation coverage, this is insufficient, as it doesn't explain the behavioral traits beyond basic functionality.

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 concise and front-loaded, with two sentences that directly state the purpose and return value. There's no unnecessary information or redundancy. However, it could be slightly improved by integrating usage context, but it's efficient and well-structured, earning a high score for brevity and 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 has 0 parameters, 100% schema coverage, and an output schema (implied by 'Returns: Dictionary'), the description is minimally adequate. It covers the basic purpose and return type, but lacks details on validation specifics, error handling, or integration with siblings. For a tool with no annotations, it should provide more behavioral context to be fully complete, so it scores as the minimum viable level.

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 doesn't add parameter details, which is appropriate. A baseline of 4 is applied since the schema fully covers the parameters (none), and the description doesn't need to compensate, but it doesn't reach 5 as it doesn't enhance parameter understanding beyond the 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: 'Validate current QuantConnect authentication configuration.' It specifies the verb 'validate' and the resource 'authentication configuration,' making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_auth_status' or 'test_quantconnect_api,' which may have overlapping functions, so it doesn't reach a score of 5.

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 (e.g., after configuring auth), exclusions, or comparisons to siblings like 'get_auth_status' or 'test_quantconnect_api.' This lack of context leaves the agent without clear usage instructions, scoring low due to the absence of explicit or implied guidelines.

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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