Server has 7 tools with basic schemas and descriptions, but significant gaps prevent higher scores. All tools have descriptions (10-80 chars, mostly adequate) and input schemas with types. However, output schemas are undocumented, LLMs cannot plan downstream calls. Error handling is minimal (generic exception catches returning error dicts). No parameter validation guidance, no enum constraints, no pagination support for list tools. Destructive tools (delete_cloud_run_service, delete_secret) lack confirmation patterns. Tool names are clear and verb-prefixed, but composition is weak: list_secrets returns bare strings instead of structured objects with IDs for chaining.

• Document output schemas for all tools. E.g., list_cloud_run_services returns {services: [{name: string, uri: string, status: string}], total_count: integer}. Include all fields agents need for downstream calls.
• Add limit and offset parameters to list_cloud_run_services and list_secrets. Default limit=20, max=100. Return total_count so agents know if more results exist.
• Implement confirmation pattern for destructive tools: add a dry_run boolean parameter (default false) or a separate confirm_delete_* tool that returns a confirmation_token, which delete_* then requires.
• Improve error messages with recovery guidance. E.g., 'Service not found in region us-central1. Call list_cloud_run_services(project_id, region) to see available services.' Classify errors as retryable (timeout, rate limit) vs user-fixable (not found, invalid input).
• Return structured objects from list_secrets: [{name: string, created_at: string, version_count: integer}] instead of bare strings. This enables agents to filter and chain without extra lookups.