Skip to main content
Glama

configure_quantconnect_auth

Set up API authentication for QuantConnect trading platform by providing user ID, API token, and optional organization ID to enable automated trading operations.

Instructions

Configure QuantConnect API authentication credentials.

Args: user_id: Your QuantConnect user ID (from email) api_token: Your QuantConnect API token (from Settings page) organization_id: Your organization ID (from organization URL)

Returns: Dictionary containing authentication configuration status

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_idYes
api_tokenYes
organization_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main tool handler function that configures QuantConnect authentication credentials, validates them, and returns the status.
    @mcp.tool()
    async def configure_quantconnect_auth(
        user_id: str, api_token: str, organization_id: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Configure QuantConnect API authentication credentials.
    
        Args:
            user_id: Your QuantConnect user ID (from email)
            api_token: Your QuantConnect API token (from Settings page)
            organization_id: Your organization ID (from organization URL)
    
        Returns:
            Dictionary containing authentication configuration status
        """
        try:
            # Configure authentication
            auth = configure_auth(user_id, api_token, organization_id)
    
            # Validate the configuration
            is_valid, message = await auth.validate_authentication()
    
            if is_valid:
                return {
                    "status": "success",
                    "message": "QuantConnect authentication configured and validated successfully",
                    "user_id": user_id,
                    "organization_id": organization_id,
                    "has_organization": organization_id is not None,
                    "authenticated": True,
                }
            else:
                return {
                    "status": "error",
                    "error": f"Authentication validation failed: {message}",
                    "user_id": user_id,
                    "organization_id": organization_id,
                    "authenticated": False,
                }
    
        except Exception as e:
            return {
                "status": "error",
                "error": str(e),
                "message": "Failed to configure QuantConnect authentication",
            }
  • Registers the authentication tools, including configure_quantconnect_auth, by calling register_auth_tools on the MCP instance.
    register_auth_tools(mcp)
  • Helper function called by the handler to set up the global QuantConnectAuth instance with provided credentials.
    def configure_auth(
        user_id: Optional[str] = None,
        api_token: Optional[str] = None,
        organization_id: Optional[str] = None,
    ) -> QuantConnectAuth:
        """
        Configure global QuantConnect authentication.
    
        Args:
            user_id: QuantConnect user ID
            api_token: QuantConnect API token
            organization_id: QuantConnect organization ID
    
        Returns:
            Configured QuantConnectAuth instance
        """
        global _auth_instance
        _auth_instance = QuantConnectAuth(user_id, api_token, organization_id)
        return _auth_instance
  • Function signature and docstring defining the input parameters and output format, serving as the tool schema.
    async def configure_quantconnect_auth(
        user_id: str, api_token: str, organization_id: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Configure QuantConnect API authentication credentials.
    
        Args:
            user_id: Your QuantConnect user ID (from email)
            api_token: Your QuantConnect API token (from Settings page)
            organization_id: Your organization ID (from organization URL)
    
        Returns:
            Dictionary containing authentication configuration status
        """
  • The @mcp.tool() decorator registers the function as an MCP tool within the register_auth_tools function.
    @mcp.tool()
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 states the tool configures credentials but fails to describe critical traits like whether this persists authentication state, requires specific permissions, or has side effects (e.g., overwriting existing config). This leaves significant gaps for a mutation tool.

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 sized and front-loaded, starting with the core purpose followed by structured parameter and return details. Every sentence adds value, though the 'Args:' and 'Returns:' sections could be integrated more seamlessly into the flow, slightly affecting structure.

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 (authentication configuration with 3 parameters) and the presence of an output schema (which covers return values), the description is moderately complete. It explains parameters well but lacks behavioral context and usage guidelines, making it adequate but with clear gaps for effective agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose and source (e.g., 'user_id: Your QuantConnect user ID (from email)'), compensating fully for the schema's lack of documentation and providing clear, actionable context for all three parameters.

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 ('Configure') and resource ('QuantConnect API authentication credentials'), distinguishing it from sibling tools like 'clear_quantconnect_auth' or 'validate_quantconnect_auth'. It precisely identifies what the tool does without being vague or tautological.

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 like 'authorize_connection' or 'test_quantconnect_api'. It lacks context about prerequisites, such as whether this should be called before other operations or if it's part of a setup flow, leaving the agent with no usage direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/taylorwilsdon/quantconnect-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server