---
title: Prompts
sidebar_position: 3
---
Reusable text templates with parameters that AI assistants can invoke via MCP. Define once, use everywhere — no code changes needed.
## How It Works
1. Create `prompts.json` with template definitions
2. MCP server registers them as available prompts
3. AI assistant (Claude Code, etc.) can invoke them by name with parameters
4. Template renders into a structured instruction for the AI
## Use Cases
- Standardize team workflows (e.g., "analyze collection before optimization")
- Create project-specific search patterns (e.g., "find code related to ticket X")
- Build guided wizards for complex operations
## Setup
1. **Create a prompts configuration file** (e.g., `prompts.json` in the project root). See `prompts.example.json` for example configurations.
2. **Configure the server** (optional — only needed for custom path):
```json
{
"mcpServers": {
"qdrant": {
"env": {
"PROMPTS_CONFIG_FILE": "/custom/path/to/prompts.json"
}
}
}
}
```
3. **Use prompts** in your AI assistant:
```bash
/mcp__tea-rags-mcp__find_similar_docs papers "neural networks" 10
```
## Template Syntax
Templates use `{{variable}}` placeholders:
- Required arguments must be provided
- Optional arguments use defaults if not specified
## Example Prompts
See `prompts.example.json` for ready-to-use prompts including:
- `setup_rag_collection` — create RAG-optimized collections
- `analyze_collection` — collection insights and recommendations
- `compare_search_methods` — semantic vs hybrid search comparison
