Skip to main content
Glama

get_auth_headers_info

Retrieve authentication header details for QuantConnect API access while protecting sensitive credentials. This tool provides header information needed for secure API requests.

Instructions

Get information about authentication headers (without exposing sensitive data).

Returns: Dictionary containing header information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function implementing the get_auth_headers_info MCP tool. It retrieves authentication header metadata (field names, presence indicators) without exposing sensitive token values, using the shared auth instance.
    @mcp.tool()
    async def get_auth_headers_info() -> Dict[str, Any]:
        """
        Get information about authentication headers (without exposing sensitive data).
    
        Returns:
            Dictionary containing header information
        """
        try:
            auth = get_auth_instance()
    
            if auth is None:
                return {"status": "error", "error": "Authentication not configured"}
    
            # Get headers (but don't expose the actual values)
            headers = auth.get_headers()
    
            return {
                "status": "success",
                "header_fields": list(headers.keys()),
                "has_authorization": "Authorization" in headers,
                "has_timestamp": "Timestamp" in headers,
                "timestamp_format": "Unix timestamp",
                "auth_method": "Basic Authentication with SHA-256 hashed token",
            }
    
        except Exception as e:
            return {
                "status": "error",
                "error": str(e),
                "message": "Failed to get authentication header information",
            }
  • Invocation of register_auth_tools which defines and registers the get_auth_headers_info tool (along with other auth tools) to the MCP server instance.
    register_auth_tools(mcp)
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 returns a dictionary without sensitive data, but lacks details on permissions, rate limits, error handling, or what specific header information is included. This is inadequate 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.

Conciseness4/5

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

The description is brief and front-loaded with the core purpose, followed by a return statement. It avoids unnecessary elaboration, though the return format could be slightly more informative.

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 no parameters, annotations, but an output schema exists, the description is minimally adequate. It covers the purpose and return type, but lacks behavioral context and usage guidelines, making it incomplete for optimal agent understanding.

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 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add param details, which is appropriate, but a baseline of 4 is given since it doesn't compensate for any gaps.

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 ('information about authentication headers'), and distinguishes it from sensitive data exposure. However, it doesn't explicitly differentiate from sibling tools like 'get_auth_status' or 'authorize_connection', which prevents a perfect score.

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, context, or exclusions, leaving the agent to infer usage from the purpose alone.

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