The server implements a single 'notify' tool with a well-structured input schema and comprehensive parameter coverage. The tool description is adequate (73 chars) but could be more actionable for LLM selection. All parameters have types and descriptions, meeting baseline requirements. However, the description lacks guidance on when to use this tool, what errors occur, and recovery paths, critical for LLM planning. The schema is well-formed with proper JSON Schema validation including enums, format constraints, and nested objects. Output documentation is completely absent, LLMs cannot understand what the tool returns, forcing them to make assumptions about success/failure responses. Error handling guidance is not visible in the tool definition itself.

• Expand tool description to 100 - 200 characters: 'Send notifications to configured webhooks (Discord, Slack, Teams, Feishu, ntfy, custom). Use for alerts, status updates, and confirmations. Returns success status and message ID, or error details if the webhook fails.'
• Document the return schema in the tool definition: e.g., {success: boolean, messageId?: string, error?: string, timestamp: ISO8601}. This enables LLMs to validate success and extract IDs for cross-referencing.
• Add error recovery guidance to the description: 'If the webhook is unreachable, check configuration and try again. If rate-limited, wait before retrying. If auth fails, verify credentials.'
• Consider renaming 'notify' to 'send_notification' for consistency with action-verb naming conventions in the baseline (get, list, create, search, update, send).
• Add a dry-run or confirmation pattern for high-risk notifications: 'Set preview=true to preview the message without sending, or add confirm_required=true to get a confirmation message ID before final send.'
• Document constraints on webhook URL validation, message size limits, and rate limits in parameter descriptions (e.g., 'message: max 4000 characters').
• Ensure the implementation returns structured output (JSON) with clear success/failure indication, not plaintext or opaque responses that force LLMs to parse unstructured output.