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MCP server for summarizing and saving conversation threads as prompts
The Prompt Saver MCP server has moderately defined tools with variable quality. Most tools have descriptions and parameter schemas, but there are notable gaps in parameter descriptions, error handling guidance, and output schema documentation. The tool names are clear and action-oriented (save_, search_, get_, update_, improve_), which is positive. However, several parameters lack detailed descriptions or constraints, and the schema definitions are incomplete in terms of return types. The server demonstrates awareness of domain best practices (mentioning AUTO-TRIGGER CONDITIONS, best practices for prompt structure), but execution is inconsistent across tools.
Get detailed prompt information including full template. **WHEN TO USE:** Call this tool immediately after a user selects a prompt from search_prompts() results. This retrieves the complete prompt template that you can then apply to their task. **AUTO-TRIGGER CONDITIONS:** - User selects a specific prompt from search results - User provides a prompt ID they want to see details for - You need the full template content to help with their current task **FOLLOW-UP:** Use the retrieved prompt_template to guide your response to the user's request.
Improve a prompt based on user feedback using AI analysis.
Save conversation as reusable prompt template. **WHEN TO USE:** Call this tool after successfully completing a complex, multi-step task that could be valuable for future similar requests. Look for conversations involving problem-solving, code generation, data analysis, creative work, or any task that produced good results and could benefit others. **AUTO-TRIGGER CONDITIONS:** - User expresses satisfaction with results ("this worked great", "perfect", "exactly what I needed") - Complex multi-step process completed successfully - Code generation, analysis, or creative work finished - User asks to "save this approach" or "remember this for later" Creates a well-structured prompt following best practices: - Identity: Defines assistant persona and goals - Instructions: Clear rules and constraints - Examples: Few-shot learning patterns - Context: Relevant data and documents - Proper formatting with Markdown and XML tags
Search prompts by use case category.
Missing or vague output schema documentation across all tools. Tools define input parameters but do not explicitly document what fields/structure they return, forcing LLMs to infer response shape.
Parameter 'conversation_messages' in save_prompt is ambiguous: accepts either JSON string or plain string. Description mentions expected JSON format but lacks validation rules or error guidance for malformed input.
update_prompt and improve_prompt_with_feedback have minimal descriptions (50 chars each), lacking WHEN TO USE guidance, AUTO-TRIGGER CONDITIONS, or explanation of how they differ from similar tools.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 55 | 2026-07-28+ | v2 |
| 2026-03-09 | D | 54 | - | v1 |
Search for relevant prompts to help with current task using text or semantic search. **WHEN TO USE:** Call this tool when you want to search for prompts using natural language queries. Works with both semantic search (if Voyage API key is available) and text search. **AUTO-TRIGGER CONDITIONS:** - User asks for help with specific problems or tasks - User mentions problems like "I need to...", "How do I...", "Help me create..." - Any request that involves searching for similar past solutions - User asks for examples, templates, or guidance on complex tasks **FOLLOW-UP:** After presenting results, use get_prompt_details() to retrieve the full template for the prompt the user selects.
Update an existing prompt with manual changes or AI-powered improvements.
Parameter descriptions are inconsistent. Some parameters (e.g., 'prompt_id' in get_prompt_details) have only bare descriptions; others lack detail on format, constraints, or allowed values.
No error handling guidance in any tool description. LLMs cannot determine if failures are retryable, user-fixable, or unrecoverable.
search_prompts and search_by_use_case accept 'limit' parameters with defaults but lack min/max constraints. No guidance on maximum allowed values or consequences of large limits.
search_by_use_case restricts use_case to enum values (code-gen, text-gen, data-analysis, creative, general) but the parameter description does not list these values, forcing LLMs to guess.