Skip to main content
Glama

MCP-LinkedIn

secrets.py1.6 kB
# src/linkedin_mcp_server/config/secrets.py """ Interactive credential prompting and secure storage for LinkedIn MCP Server. This module handles interactive credential collection from users and securely stores them in the system keyring. It provides a user-friendly interface for credential input while ensuring security through proper keyring integration. Key Functions: - Interactive credential prompting with secure password input - Automatic storage of credentials in system keyring - User-friendly error handling and feedback - Integration with the keyring providers for secure storage """ import logging from typing import Dict import inquirer # type: ignore from .providers import ( get_keyring_name, save_credentials_to_keyring, ) logger = logging.getLogger(__name__) def prompt_for_credentials() -> Dict[str, str]: """Prompt user for LinkedIn credentials and store them securely.""" print(f"🔑 LinkedIn credentials required (will be stored in {get_keyring_name()})") questions = [ inquirer.Text("email", message="LinkedIn Email"), inquirer.Password("password", message="LinkedIn Password"), ] credentials: Dict[str, str] = inquirer.prompt(questions) if not credentials: raise KeyboardInterrupt("Credential input was cancelled") # Store credentials securely in keyring if save_credentials_to_keyring(credentials["email"], credentials["password"]): logger.info("Credentials stored securely in keyring") else: logger.warning("Could not store credentials in system keyring") return credentials

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/Kappasig920/MCP-LinkedIn'

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