PrompTalk defines 6 tools with basic schemas and descriptions, but falls short of production quality. All tools have descriptions (10-100 chars, mostly adequate) and input schemas with required fields. However, parameter descriptions are sparse or missing entirely. Most tools lack output schema documentation. No error handling guidance, no parameter constraints (enums, ranges), and no indication of idempotency or side effects. Tool names are reasonably clear (compile_prompt, lint_prompt, score_prompt) but descriptions are terse and lack WHEN/WHY context. Parameters like 'modelId' and 'profileId' are optional but undocumented, LLMs cannot infer valid values. No pagination, no batch operations, no recovery guidance. This is typical of early-stage community servers.

• Document output schema for each tool. E.g., compile_prompt should return {compiled_prompt: string, optimization_notes: string[], model_recommendations: string[]}. This enables LLMs to chain calls and extract typed fields.
• Add descriptions to optional parameters. For modelId, list valid model names or link to a discovery tool. For profileId, enumerate valid profiles (coding, reasoning, creative) as an enum.
• Expand tool descriptions to 100-200 chars. Include WHEN to call: 'Use compile_prompt to refine raw user input into a structured system instruction. Call this before lint_prompt or score_prompt to ensure well-formed prompts.'
• Add error handling guidance. E.g., 'If the Gemini API returns a quota error, wait 60 seconds and retry. If the prompt is malformed, return a validation error with the offending section highlighted.'
• Implement pagination for search_templates. Add limit (default 20, max 100) and offset parameters. Return total_count and next_offset so agents can iterate through large result sets.