ClarifyPrompt MCP

<a href="https://glama.ai/mcp/servers/LumabyteCo/clarifyprompt-mcp"><img width="380" height="200" src="https://glama.ai/mcp/servers/LumabyteCo/clarifyprompt-mcp/badge" alt="ClarifyPrompt MCP server" /></a>

An MCP server that transforms vague prompts into platform-optimized prompts for 58+ AI platforms across 7 categories — with support for registering custom platforms and providing markdown instruction files.

Send a raw prompt. Get back a version specifically optimized for Midjourney, DALL-E, Sora, Runway, ElevenLabs, Claude, ChatGPT, or any of the 58+ supported platforms — with the right syntax, parameters, and structure each platform expects. Register your own platforms and provide custom optimization instructions via .md files.

How It Works

You write:    "a dragon flying over a castle at sunset"

ClarifyPrompt returns (for Midjourney):
  "a majestic dragon flying over a medieval castle at sunset
   --ar 16:9 --v 6.1 --style raw --q 2 --chaos 30 --s 700"

ClarifyPrompt returns (for DALL-E):
  "A majestic dragon flying over a castle at sunset. Size: 1024x1024"

Same prompt, different platform, completely different output. ClarifyPrompt knows what each platform expects.

Quick Start

With Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "clarifyprompt": {
      "command": "npx",
      "args": ["-y", "clarifyprompt-mcp"],
      "env": {
        "LLM_API_URL": "http://localhost:11434/v1",
        "LLM_MODEL": "qwen2.5:7b"
      }
    }
  }
}

With Claude Code

claude mcp add clarifyprompt -- npx -y clarifyprompt-mcp

Set the environment variables in your shell before launching:

export LLM_API_URL=http://localhost:11434/v1
export LLM_MODEL=qwen2.5:7b

With Cursor

Add to your .cursor/mcp.json:

{
  "mcpServers": {
    "clarifyprompt": {
      "command": "npx",
      "args": ["-y", "clarifyprompt-mcp"],
      "env": {
        "LLM_API_URL": "http://localhost:11434/v1",
        "LLM_MODEL": "qwen2.5:7b"
      }
    }
  }
}

Supported Platforms (58+ built-in, unlimited custom)

Category	Platforms	Default
Image (10)	Midjourney, DALL-E 3, Stable Diffusion, Flux, Ideogram, Leonardo AI, Adobe Firefly, Grok Aurora, Google Imagen 3, Recraft	Midjourney
Video (11)	Sora, Runway Gen-3, Pika Labs, Kling AI, Luma, Minimax/Hailuo, Google Veo 2, Wan, HeyGen, Synthesia, CogVideoX	Runway
Chat (9)	Claude, ChatGPT, Gemini, Llama, DeepSeek, Qwen, Kimi, GLM, Minimax	Claude
Code (9)	Claude, ChatGPT, Cursor, GitHub Copilot, Windsurf, DeepSeek Coder, Qwen Coder, Codestral, Gemini	Claude
Document (8)	Claude, ChatGPT, Gemini, Jasper, Copy.ai, Notion AI, Grammarly, Writesonic	Claude
Voice (7)	ElevenLabs, OpenAI TTS, Fish Audio, Sesame, Google TTS, PlayHT, Kokoro	ElevenLabs
Music (4)	Suno AI, Udio, Stable Audio, MusicGen	Suno

Tools

optimize_prompt

The main tool. Optimizes a prompt for a specific AI platform.

{
  "prompt": "a cat sitting on a windowsill",
  "category": "image",
  "platform": "midjourney",
  "mode": "concise"
}

All parameters except prompt are optional. When category and platform are omitted, ClarifyPrompt auto-detects them from the prompt content.

Three calling modes:

Mode	Example
Zero-config	{ "prompt": "sunset over mountains" }
Category only	{ "prompt": "...", "category": "image" }
Fully explicit	{ "prompt": "...", "category": "image", "platform": "dall-e" }

Parameters:

Parameter	Required	Description
prompt	Yes	The prompt to optimize
category	No	chat, image, video, voice, music, code, document. Auto-detected when omitted.
platform	No	Platform ID (e.g. midjourney, dall-e, sora, claude). Uses category default when omitted.
mode	No	Output style: concise, detailed, structured, step-by-step, bullet-points, technical, simple. Default: detailed.
enrich_context	No	Set true to use web search for context enrichment. Default: false.

Response:

{
  "originalPrompt": "a dragon flying over a castle at sunset",
  "optimizedPrompt": "a majestic dragon flying over a medieval castle at sunset --ar 16:9 --v 6.1 --style raw --q 2 --s 700",
  "category": "image",
  "platform": "midjourney",
  "mode": "concise",
  "detection": {
    "autoDetected": true,
    "detectedCategory": "image",
    "detectedPlatform": "midjourney",
    "confidence": "high"
  },
  "metadata": {
    "model": "qwen2.5:14b-instruct-q4_K_M",
    "processingTimeMs": 3911,
    "strategy": "ImageStrategy"
  }
}

The detection field only appears when auto-detection was used. When category and platform are provided explicitly, detection is skipped.

list_categories

Lists all 7 categories with platform counts (built-in and custom) and defaults.

list_platforms

Lists available platforms for a given category, including custom registered platforms. Shows which is the default and whether custom instructions are configured.

list_modes

Lists all 7 output modes with descriptions.

register_platform

Register a new custom AI platform for prompt optimization.

{
  "id": "my-llm",
  "category": "chat",
  "label": "My Custom LLM",
  "description": "Internal fine-tuned model",
  "syntax_hints": ["JSON mode", "max 2000 tokens"],
  "instructions": "Always use structured output format",
  "instructions_file": "my-llm.md"
}

Parameter	Required	Description
id	Yes	Unique ID (lowercase, alphanumeric with hyphens)
category	Yes	Category this platform belongs to
label	Yes	Human-readable platform name
description	Yes	Short description
syntax_hints	No	Platform-specific syntax hints
instructions	No	Inline optimization instructions
instructions_file	No	Path to a .md file with detailed instructions

update_platform

Update a custom platform or add instruction overrides to a built-in platform.

For built-in platforms (e.g. Midjourney, Claude), you can add custom instructions and extra syntax hints without modifying the originals:

{
  "id": "midjourney",
  "category": "image",
  "instructions": "Always use --v 6.1, prefer --style raw",
  "syntax_hints_append": ["--no plants", "--tile for patterns"]
}

For custom platforms, all fields can be updated.

unregister_platform

Remove a custom platform or clear instruction overrides from a built-in platform.

{
  "id": "my-llm",
  "category": "chat"
}

For built-in platforms, use remove_override_only: true to clear your custom instructions without affecting the platform itself.

Custom Platforms & Instructions

ClarifyPrompt supports registering custom platforms and providing optimization instructions — similar to how .cursorrules or CLAUDE.md guide AI behavior.

How It Works

1. Register a custom platform via register_platform
2. Provide instructions inline or as a .md file
3. Optimize prompts targeting your custom platform — instructions are injected into the optimization pipeline

Instruction Files

Instructions can be provided as markdown files stored at ~/.clarifyprompt/instructions/:

~/.clarifyprompt/
  config.json                    # custom platforms + overrides
  instructions/
    my-llm.md                   # instructions for custom platform
    midjourney-overrides.md     # extra instructions for built-in platform

Example instruction file (my-llm.md):

# My Custom LLM Instructions

## Response Format
- Always output valid JSON
- Include a "reasoning" field before the answer

## Constraints
- Max 2000 tokens
- Temperature should be set low (0.1-0.3) for factual queries

## Style
- Be concise and technical
- Avoid filler phrases

Override Built-in Platforms

You can add custom instructions to any of the 58 built-in platforms using update_platform. This lets you customize how prompts are optimized for platforms like Midjourney, Claude, or Sora without modifying the defaults.

Config Directory

The config directory defaults to ~/.clarifyprompt/ and can be changed via the CLARIFYPROMPT_CONFIG_DIR environment variable. Custom platforms and overrides persist across server restarts.

LLM Configuration

ClarifyPrompt uses an LLM to optimize prompts. It works with any OpenAI-compatible API and with the Anthropic API directly.

Environment Variables

Variable	Required	Description
LLM_API_URL	Yes	API endpoint URL
LLM_API_KEY	Depends	API key (not needed for local Ollama)
LLM_MODEL	Yes	Model name/ID

Provider Examples

Ollama (local, free):

LLM_API_URL=http://localhost:11434/v1
LLM_MODEL=qwen2.5:7b

OpenAI:

LLM_API_URL=https://api.openai.com/v1
LLM_API_KEY=sk-...
LLM_MODEL=gpt-4o

Anthropic Claude:

LLM_API_URL=https://api.anthropic.com/v1
LLM_API_KEY=sk-ant-...
LLM_MODEL=claude-sonnet-4-20250514

Google Gemini:

LLM_API_URL=https://generativelanguage.googleapis.com/v1beta/openai
LLM_API_KEY=your-gemini-key
LLM_MODEL=gemini-2.0-flash

Groq:

LLM_API_URL=https://api.groq.com/openai/v1
LLM_API_KEY=gsk_...
LLM_MODEL=llama-3.3-70b-versatile

DeepSeek:

LLM_API_URL=https://api.deepseek.com/v1
LLM_API_KEY=your-deepseek-key
LLM_MODEL=deepseek-chat

OpenRouter (any model):

LLM_API_URL=https://openrouter.ai/api/v1
LLM_API_KEY=your-openrouter-key
LLM_MODEL=anthropic/claude-sonnet-4

See .env.example for the full list of 20+ supported providers including Together AI, Fireworks, Mistral, xAI, Cohere, Perplexity, LM Studio, vLLM, LocalAI, Jan, GPT4All, and more.

Web Search (Optional)

Enable context enrichment by setting enrich_context: true in your optimize_prompt call. ClarifyPrompt will search the web for relevant context before optimizing.

Supported search providers:

Provider	Variable	URL
Tavily (default)	SEARCH_API_KEY	tavily.com
Brave Search	SEARCH_API_KEY	brave.com/search/api
Serper	SEARCH_API_KEY	serper.dev
SerpAPI	SEARCH_API_KEY	serpapi.com
Exa	SEARCH_API_KEY	exa.ai
SearXNG (self-hosted)	—	github.com/searxng/searxng

SEARCH_PROVIDER=tavily
SEARCH_API_KEY=your-key

Before and After

Image (Midjourney)

Before: "a cat sitting on a windowsill"

After:  "a tabby cat sitting on a sunlit windowsill, warm golden hour
         lighting, shallow depth of field, dust particles in light beams,
         cozy interior background, shot on 35mm film, warm amber color
         palette --ar 16:9 --v 6.1 --style raw --q 2"

Video (Sora)

Before: "a timelapse of a city"

After:  "Cinematic timelapse of a sprawling metropolitan skyline
         transitioning from golden hour to blue hour to full night.
         Camera slowly dollies forward from an elevated vantage point.
         Light trails from traffic appear as the city illuminates.
         Clouds move rapidly overhead. Duration: 10s.
         Style: documentary cinematography, 4K."

Code (Claude)

Before: "write a function to validate emails"

After:  "Write a TypeScript function `validateEmail(input: string): boolean`
         that validates email addresses against RFC 5322. Handle edge cases:
         quoted local parts, IP address domains, internationalized domain
         names. Return boolean, no exceptions. Include JSDoc with examples
         of valid and invalid inputs. No external dependencies."

Music (Suno)

Before: "compose a chill lo-fi beat for studying"

After:  "Compose an instrumental chill lo-fi beat for studying.
         [Tempo: medium] [Genre: lo-fi] [Length: 2 minutes]"

Architecture

clarifyprompt-mcp/
  src/
    index.ts                           MCP server entry point (7 tools, 1 resource)
    engine/
      config/
        categories.ts                  7 categories, 58 platforms, 7 modes
        persistence.ts                 ConfigStore — JSON config + .md file loading
        registry.ts                    PlatformRegistry — merges built-in + custom platforms
      llm/client.ts                    Multi-provider LLM client (OpenAI + Anthropic)
      search/client.ts                 Web search (6 providers)
      optimization/
        engine.ts                      Core orchestrator + auto-detection
        types.ts                       TypeScript interfaces
        strategies/
          base.ts                      Abstract base strategy
          chat.ts                      9 platforms
          image.ts                     10 platforms
          video.ts                     11 platforms
          voice.ts                     7 platforms
          music.ts                     4 platforms
          code.ts                      9 platforms
          document.ts                  8 platforms

Docker

docker build -t clarifyprompt-mcp .
docker run -e LLM_API_URL=http://host.docker.internal:11434/v1 -e LLM_MODEL=qwen2.5:7b clarifyprompt-mcp

Development

git clone https://github.com/LumabyteCo/clarifyprompt-mcp.git
cd clarifyprompt-mcp
npm install
npm run build

Test with MCP Inspector:

npx @modelcontextprotocol/inspector node dist/index.js

Set environment variables in the Inspector's "Environment Variables" section before connecting.

License

Apache-2.0