Prompt Saver MCP Server

MCP server that converts successful conversation threads into prompts that can be used for future tasks.

Based on the principle that the most important artifact of your LLM interactions is what you did to produce the results, not the results themselves (see The New Code). And also considering that LLMs are probably already better prompt engineers than humans.

prompt-saver-mcp-demo.mp4

Tools

save_prompt

Summarizes, categorizes, and converts conversation history into a markdown formatted prompt template.

Run upon completion of a successful complex task to build your prompt library.

Parameters:

• conversation_messages (string): JSON string containing the conversation history
• task_description (optional string): Description of the task being performed
• context_info (optional string): Additional context about the conversation

search_prompts

Retrieves prompts from the database using semantic search (if Voyage API key is available) or text search as fallback. Returns the most relevant prompts for user selection.

Parameters:

• query (string): Description of the problem or task you need help with
• limit (optional int): Maximum number of prompts to return (default: 3)

improve_prompt_from_feedback

Summarizes feedback during the conversation and updates the prompt based on the feedback and conversation context.

Parameters:

• prompt_id (string): ID of the prompt to improve
• feedback (string): User feedback about the prompt's effectiveness
• conversation_context (optional string): Context from the conversation where the prompt was used

update_prompt

For manual updates to an existing prompt.

Parameters:

• prompt_id (string): ID of the prompt to update
• change_description (string): Description of what was changed and why
• summary (optional string): Updated summary
• prompt_template (optional string): Updated prompt template
• history (optional string): Updated history
• use_case (optional string): Updated use case

get_prompt_details

Get detailed information about a specific prompt including its full template, history, and metadata. Used to view the complete prompt before applying it.

Parameters:

• prompt_id (string): ID of the prompt to retrieve

search_prompts_by_use_case

Search for prompts by their use case category (e.g., 'code-gen', 'text-gen', 'data-analysis'). Efficient category-based search that works independently of embedding services.

Parameters:

• use_case (string): The use case category to search for
• limit (optional int): Maximum number of results to return (default: 5)

Prompt Engineering Best Practices

The generated prompt templates use the following prompt engineering techniques:

Structure

Templates are organized in this order:

1. Identity: Defines the assistant's persona and goals
2. Instructions: Provides clear rules and constraints
3. Examples: Shows desired input/output patterns (few-shot learning)
4. Context: Adds relevant data and documents

Formatting

• Markdown headers (#) and lists (*) for logical hierarchy
• XML tags (<example>) to separate content sections
• Message roles (developer/user/assistant) where appropriate
• Placeholders ({variable}) for customizable inputs

This ensures the saved prompts are well-structured, reusable, and effective for future tasks.

System Prompt Configuration

To maximize the value of this MCP server, add the following instructions to your LLM interface's system prompt:

Always search for relevant prompts before starting any large or complex tasks.

Upon successful completion of a task, always ask if I want to save the conversation as a prompt.

Upon successful completion of a task with a prompt, always ask if I want to update the prompt.

This helps ensure that the LLM runs the relevant tools without you explicitly asking.

💡 Tip: For enhanced MongoDB management, consider using the MongoDB MCP Server alongside this prompt saver. It provides direct MongoDB operations and can help you manage your prompt database more effectively.

Installation

1. Clone the repository:

   git clone <repository-url>
   cd prompt-saver-mcp

2. Install dependencies:

   uv sync
   # or with pip:
   pip install -e .

3. Set up MongoDB Atlas:

   • Create a MongoDB Atlas cluster
   • Create a database named prompt_saver
   • (Optional) Create a vector search index on the embedding field (2048 dimensions, dotProduct similarity) for semantic search

4. Configure environment variables:

   cp .env.example .env
   # Edit .env with your API keys and MongoDB Atlas URI

   Required for all configurations:

   • MONGODB_URI: MongoDB Atlas connection string

   Optional:

   • VOYAGE_AI_API_KEY: Voyage AI API key (enables semantic search)

   Choose one LLM provider:

   • Azure OpenAI: AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT
   • OpenAI: OPENAI_API_KEY
   • Anthropic: ANTHROPIC_API_KEY

Configuration

Environment Variables

Core Configuration

Variable	Description	Default
MONGODB_URI	MongoDB Atlas connection URI	Required
MONGODB_DATABASE	Database name	prompt_saver
MONGODB_COLLECTION	Collection name	prompts
VOYAGE_AI_API_KEY	Voyage AI API key (enables semantic search)	Optional
VOYAGE_AI_EMBEDDING_MODEL	Embedding model	voyage-3-large

LLM Provider Configuration

Variable	Description	Default
LLM_PROVIDER	LLM provider: azure_openai, openai, or anthropic	azure_openai

Provider Options:

• Azure OpenAI: Enterprise-grade with enhanced security and compliance
• OpenAI: Direct access to latest models with simple API key setup
• Anthropic: Claude models with strong reasoning capabilities

Azure OpenAI (when LLM_PROVIDER=azure_openai)

Variable	Description	Default
AZURE_OPENAI_API_KEY	Azure OpenAI API key	Required
AZURE_OPENAI_ENDPOINT	Azure OpenAI endpoint	Required
AZURE_OPENAI_MODEL	Model deployment name	gpt-4o

OpenAI (when LLM_PROVIDER=openai)

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key	Required
OPENAI_MODEL	Model name	gpt-4o

Anthropic (when LLM_PROVIDER=anthropic)

Variable	Description	Default
ANTHROPIC_API_KEY	Anthropic API key	Required
ANTHROPIC_MODEL	Model name	claude-sonnet-4-20250514

Claude Desktop Integration

Add to your Claude Desktop configuration file. Choose one of the following configurations based on your preferred LLM provider:

Azure OpenAI Configuration

{
  "mcpServers": {
    "prompt-saver": {
      "command": "uv",
      "args": ["run", "python", "-m", "prompt_saver_mcp.server", "stdio"],
      "cwd": "/path/to/your/prompt-saver-mcp",
      "env": {
        "MONGODB_URI": "mongodb+srv://username:[email protected]/",
        "MONGODB_DATABASE": "prompt_saver",
        "VOYAGE_AI_API_KEY": "your_voyage_ai_api_key_here",
        "LLM_PROVIDER": "azure_openai",
        "AZURE_OPENAI_API_KEY": "your_azure_openai_api_key_here",
        "AZURE_OPENAI_ENDPOINT": "https://your-resource.openai.azure.com/",
        "AZURE_OPENAI_MODEL": "gpt-4o"
      }
    }
  }
}

OpenAI Configuration

{
  "mcpServers": {
    "prompt-saver": {
      "command": "uv",
      "args": ["run", "python", "-m", "prompt_saver_mcp.server", "stdio"],
      "cwd": "/path/to/your/prompt-saver-mcp",
      "env": {
        "MONGODB_URI": "mongodb+srv://username:[email protected]/",
        "MONGODB_DATABASE": "prompt_saver",
        "VOYAGE_AI_API_KEY": "your_voyage_ai_api_key_here",
        "LLM_PROVIDER": "openai",
        "OPENAI_API_KEY": "your_openai_api_key_here",
        "OPENAI_MODEL": "gpt-4o"
      }
    }
  }
}

Anthropic Configuration

{
  "mcpServers": {
    "prompt-saver": {
      "command": "uv",
      "args": ["run", "python", "-m", "prompt_saver_mcp.server", "stdio"],
      "cwd": "/path/to/your/prompt-saver-mcp",
      "env": {
        "MONGODB_URI": "mongodb+srv://username:[email protected]/",
        "MONGODB_DATABASE": "prompt_saver",
        "VOYAGE_AI_API_KEY": "your_voyage_ai_api_key_here",
        "LLM_PROVIDER": "anthropic",
        "ANTHROPIC_API_KEY": "your_anthropic_api_key_here",
        "ANTHROPIC_MODEL": "claude-sonnet-4-20250514"
      }
    }
  }
}

Note: For Anthropic support, install the anthropic package: pip install anthropic

Usage Examples

1. Saving a Prompt

After completing a complex task, save the conversation as a reusable prompt:

# Example conversation messages (JSON format)
conversation = [
    {"role": "user", "content": "Help me create a Python function to parse CSV files"},
    {"role": "assistant", "content": "I'll help you create a robust CSV parser..."},
    # ... more conversation
]

# Save the prompt
save_prompt(
    conversation_messages=json.dumps(conversation),
    task_description="Creating a CSV parser function",
    context_info="Successfully created a parser with error handling"
)

2. Finding and Using a Prompt

Search for relevant prompts when starting a new task:

# Search for prompts
result = search_prompts("I need help with data processing in Python")

# The tool will return the most relevant prompts and ask you to select one

3. Updating a Prompt

After using a prompt, update it based on your experience:

update_prompt(
    prompt_id="prompt_id_here",
    change_description="Added error handling examples",
    prompt_template="Updated template with better error handling..."
)

4. Getting Prompt Details

Retrieve the full details of a specific prompt:

# Get complete prompt information
result = get_prompt_details("prompt_id_here")
print(result["prompt"]["prompt_template"])  # View the full template

5. Improving a Prompt with Feedback

Use AI to automatically improve a prompt based on feedback:

improve_prompt_from_feedback(
    prompt_id="prompt_id_here",
    feedback="The prompt worked well but could use more specific examples for edge cases",
    conversation_context="Used for debugging a complex API integration issue"
)

6. Searching by Use Case

Find prompts for specific types of tasks:

# Find all code generation prompts
result = search_prompts_by_use_case("code-gen", limit=5)

# Find data analysis prompts
result = search_prompts_by_use_case("data-analysis", limit=3)

Database Schema

Each prompt is stored with the following structure:

{
    "_id": ObjectId,
    "use_case": str,  # "code-gen", "text-gen", "data-analysis", "creative", "general"
    "summary": str,   # Summary of the prompt and its use case
    "prompt_template": str,  # Universal problem-solving prompt template
    "history": str,   # Summary of steps taken and end result
    "embedding": List[float],  # Vector embeddings of the summary
    "last_updated": datetime,
    "num_updates": int,
    "changelog": List[str]  # List of changes made to this prompt
}

License

MIT License - see LICENSE file for details.