Swift Prompter

An MCP server implementation that brings Google's research-backed prompt engineering techniques directly to Claude. Swift Prompter operationalizes the strategies from Google's Prompt Engineering whitepaper, delivering proven patterns for more effective AI interactions.

Features

• Research-Backed Templates: Pre-built implementations of Google's recommended prompt patterns
• Guided Reasoning Patterns: Chain-of-Thought, ReAct, and Step-Back techniques for complex problem solving
• Systematic Process: Framework for selecting optimal prompt patterns based on task type
• Context Efficiency: Monitors token usage to maximize reasoning space
• Zero Configuration: Ready-to-use patterns with no template creation needed

Tools

• list-templates

  • Lists available prompt templates
  • Inputs:
    • search (string, optional): Filter templates by name or description
    • tag (string, optional): Filter templates by tag

• get-template

  • Retrieves a specific template by ID
  • Inputs:
    • template_id (string): ID of the template to retrieve

• build-prompt

  • Constructs optimized prompts from templates and input values
  • Inputs:
    • template_id (string): Template to use
    • inputs (object): Values for template variables

Implemented Techniques from Google's Whitepaper

Swift Prompter provides ready-to-use implementations of key techniques from Google's prompt engineering research:

• Chain-of-Thought: Improves reasoning by guiding Claude through step-by-step thinking
• ReAct: Combines reasoning and action in a structured thought-action-observation loop
• Role Prompting: Establishes specific expertise contexts for specialized tasks
• Few-Shot Learning: Provides examples for Claude to follow similar patterns
• Self-Consistency: Generates multiple reasoning paths to verify consistency
• Step-Back Prompting: Tackles complex problems by examining from higher abstraction levels
• Structured Output: Enforces JSON or other specific format requirements

Each technique is implemented as a pre-configured template, optimized based on Google's research findings.

Configuration

Docker

The docker image is available on Docker Hub:

docker pull lumixlabs/swift-prompter:latest

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "swift-prompter": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-w",
        "/",
        "lumixlabs/swift-prompter"
      ]
    }
  }
}

Workflow Implementation

Swift Prompter enforces the research-backed workflow for optimal AI responses:

1. Technique Selection: Automatically identifies the best prompt pattern for each task type
2. Structured Prompting: Applies the selected technique with proper framing and constraints
3. Guided Execution: Directs Claude to follow specific cognitive processes based on the task
4. Context Management: Optimizes token usage to maximize reasoning space

Scientific Foundation

This implementation is based directly on Google's comprehensive prompt engineering research:

• Documented techniques from "Prompt Engineering by Lee Boonstra" (Google, 2024)
• Empirically validated patterns showing 20-50% performance improvements on reasoning tasks
• Systematic approach to selecting techniques based on task requirements

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.