A Flask web application that demonstrates how to use Google's Gemini Models and Pydantic AI to quickly create Google Forms from Natural Language Instructions
Server has 4 tools with basic descriptions and input schemas, but significant quality gaps. Tool names follow verb_noun convention (generate_form, create_google_form, list_user_forms, get_form_details), which is good. However, descriptions are minimal (10-50 chars), parameter descriptions are generic, and output schemas are completely undocumented. No error handling guidance, no pagination support for list_user_forms, and credentials_info parameter violates secret-injection pattern by exposing OAuth objects as parameters. Tools lack actionable error messages and recovery guidance.
Create a Google Form from schema.
Generate a Google Form structure from natural language description.
Get detailed information about a specific Google Form.
List Google Forms owned by the authenticated user.
Credentials exposed as tool parameters (credentials_info object). OAuth tokens and secrets must never appear in tool parameters, they are logged in traces and prompt history. Use server-side secret injection via environment variables or session management.
Output schemas completely undocumented. No documentation of what fields are returned by any tool. LLMs cannot plan downstream calls or extract required data without knowing response structure. Document return types for all tools.
Parameter descriptions are generic and lack actionable constraints. 'OAuth credentials dictionary' and 'Form schema with title, description, and fields' do not explain format, required fields, or valid values. Descriptions should be 50-200 chars with specific guidance.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 47 | <=2025-11-25 | v2 |
list_user_forms lacks pagination support. No limit, offset, or page_size parameters documented. Returning all forms without pagination risks context window exhaustion and poor LLM reasoning. Add max_results parameter (already present but undocumented) and document pagination behavior.
No error handling guidance. Tools provide no recovery hints for common failures (auth failure, form not found, quota exceeded). Error responses should tell LLMs what to do next and categorize errors as retryable, user-fixable, or fatal.