Teleprompter
An MCP server that manages and exposes tools to allow prompt re-use with LLMs.
Table of Contents
- Features
- MCP Configuration
- Usage Examples
- Environment Variables
- Testing
- Contributing
- License
- Acknowledgements
Features
- Prompt Storage & Reuse: Store, search, and retrieve prompt templates for LLMs.
- MCP Server: Exposes prompt tools via the Model Context Protocol (MCP).
- Prompt Variables: Supports template variables (e.g.,
{{name}}) for dynamic prompt generation. - Search: Fast full-text search over stored prompts using MiniSearch.
- TypeScript: Modern, type-safe codebase.
- Extensive Testing: Includes unit and integration tests with Vitest.
MCP Configuration
To use Teleprompter with your LLM client, add this configuration:
{
"mcpServers": {
"teleprompter": {
"command": "npx",
"args": ["-y", "mcp-teleprompter"],
"env": {
"PROMPT_STORAGE_PATH": "/path/to/your/prompts-directory"
}
}
}
}
Note: Replace /path/to/your/prompts-directory with the absolute path where you want prompts stored.
Usage Examples
Once configured, you can use Teleprompter with your LLM by using prompt tags in your conversations. Here's a detailed example that shows how it solves the problem of repeating complex instructions:
🎵 Music Discovery on Spotify
The Problem: Every time you want music recommendations, you have to remind your LLM of all your preferences and constraints:
- "Don't suggest songs I already have in my playlists"
- "Avoid explicit lyrics"
- "Add songs to my queue for review, not directly to playlists"
- "Focus on discovering new artists, not just popular hits"
- "Consider my current activity and mood"
- "Provide brief explanations for why each song fits"
The Solution: Create a prompt that captures all these instructions once.
Creating the prompt: Ask your LLM: "Create a prompt called 'spotify-discover' that helps me find new music with all my specific preferences and workflow requirements."
This creates a comprehensive template like:
I'm looking for music recommendations for Spotify based on:
**Current mood:** {{mood}}
**Activity/setting:** {{activity}}
**Preferred genres:** {{genres}}
**Recent artists I've enjoyed:** {{recent_artists}}
**Important constraints:**
- DO NOT suggest songs I already have in my existing playlists
- Avoid explicit lyrics (clean versions only)
- Focus on discovering new/lesser-known artists, not just popular hits
- Provide 5-7 song recommendations maximum
**Workflow:**
- Add recommendations to my Spotify queue (not directly to playlists)
- I'll review and save the ones I like to appropriate playlists later
**For each recommendation, include:**
- Artist and song name
- Brief explanation (1-2 sentences) of why it fits my current mood/activity
- Similar artists I might also enjoy
Please help me discover music that matches this vibe while following these preferences.
Using it:
>> spotify-discover
Now you just fill in your current mood and activity, and get perfectly tailored recommendations that follow all your rules—no need to repeat your constraints every time.
🔄 Other Common Use Cases
📋 Work Ticket Management
- Create prompts for JIRA/Linear ticket formatting with your team's specific requirements
- Include standard fields, priority levels, acceptance criteria templates
- Avoid repeating your company's ticket standards every time
📧 Email Templates
- Customer support responses with your company's tone and required disclaimers
- Follow-up sequences that match your communication style
- Automated inclusion of signatures, links, and standard information
📝 Code Review Guidelines
- Technical review checklists with your team's specific standards
- Security considerations and performance criteria
- Documentation requirements and testing expectations
The common thread: stop repeating yourself. If you find yourself giving the same detailed instructions to your LLM repeatedly, create a prompt for it.
🔍 Discovering Existing Prompts
You can search your prompt library:
Can you search my prompts for "productivity" or "task management"?
Or list all available prompts:
What prompts do I have available?
✏️ Manual Editing
Prompts are stored as simple markdown files in your PROMPT_STORAGE_PATH directory. You can also create and edit them directly with your favorite text editor:
- Each prompt is saved as
{id}.mdin your prompts directory - Use
{{variable_name}}syntax for template variables - Standard markdown formatting is supported
- File changes are automatically picked up by the server
💡 Best Practices
-
Use descriptive IDs: Choose prompt IDs that clearly indicate their purpose (e.g.,
meeting-notes,code-review-checklist) -
Include helpful variables: Use
{{variable_name}}for dynamic content that changes each time you use the prompt -
Organize by category: Consider using prefixes like
task-,content-,analysis-to group related prompts
Testing
Run all tests:
npm test
Run tests with coverage:
npm run test:coverage
Tests are written with Vitest. Coverage reports are generated in the coverage/ directory.
Contributing
Contributions are welcome! Please:
- Follow the existing code style (see
.prettierrc.jsonand.eslintrc.mjs). - Add tests for new features or bug fixes.
License
This project is licensed under the MIT License. See LICENSE for details.
Acknowledgements
Made with ❤️ by John Anderson