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Prompts

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):

{
  "mcpServers": {
    "tea-rags": {
      "env": {
        "PROMPTS_CONFIG_FILE": "/custom/path/to/prompts.json"
      }
    }
  }
}
  1. Use prompts in your AI assistant (example — run a prompt defined in prompts.example.json):
/mcp__tea-rags__analyze_and_optimize code_27622aef

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_and_optimize — collection insights and recommendations
  • compare_search_strategies — semantic vs hybrid search comparison
  • migrate_to_hybrid — upgrade dense-only collections to hybrid search
  • debug_search_quality — diagnose why a query returns poor results
  • build_knowledge_base — end-to-end RAG knowledge base assembly