mcp-teleprompter

An MCP server that manages and exposes tools to allow prompt re-use with LLMs.

• Features
• MCP Configuration
• Usage Examples
• Environment Variables
• Testing
• Contributing
• License
• Acknowledgements

• 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.

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.

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:

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.

📋 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.

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?

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}.md in your prompts directory
• Use {{variable_name}} syntax for template variables
• Standard markdown formatting is supported
• File changes are automatically picked up by the server

1. Use descriptive IDs: Choose prompt IDs that clearly indicate their purpose (e.g., meeting-notes, code-review-checklist)

2. Include helpful variables: Use {{variable_name}} for dynamic content that changes each time you use the prompt

3. Organize by category: Consider using prefixes like task-, content-, analysis- to group related prompts

Run all tests:

Run tests with coverage:

Tests are written with Vitest. Coverage reports are generated in the coverage/ directory.

Contributions are welcome! Please:

• Follow the existing code style (see .prettierrc.json and .eslintrc.mjs).
• Add tests for new features or bug fixes.

This project is licensed under the MIT License. See LICENSE for details.

• Model Context Protocol (MCP)
• MiniSearch
• Vitest
• Zod
• dotenv

Made with ❤️ by John Anderson