ClarifyPrompt MCP

A context-aware MCP prompt compiler that transforms vague prompts into platform-optimized prompts for 58+ AI platforms across 7 categories — grounded in your workspace signals (CLAUDE.md, AGENTS.md, .cursorrules, package.json), resolved intent, and the capabilities of the target model.

Send a raw prompt. ClarifyPrompt gathers the right context, resolves what you're actually trying to do, and returns a version specifically optimized for Midjourney, DALL-E, Sora, Runway, ElevenLabs, Claude, ChatGPT, Cursor, or any of the 58+ supported platforms — with the right syntax, parameters, structure, and grounding.

New in 1.2.0: Context Engine — automatic workspace signal gathering, intent resolution, target-model capability hints, and local JSONL tracing. See CHANGELOG.md.

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 — and in 1.2.0, it also knows what you're working on.

What's in the box (1.2.0)

• Context Engine — auto-gathers workspace rules (CLAUDE.md, AGENTS.md, .cursorrules, .clinerules, clarify.md), detects frameworks and languages from package.json and sibling manifests, tracks an active file excerpt, and maintains a per-session ring buffer of recent optimizations and their outcomes.
• Unified PromptAnalyzer — one LLM call produces { category, intent, recommendedMode, confidence } together. 10 intents: production-code, brand-voice, stakeholder-comm, data-extract, creative-media, technical-spec, analysis, quick-draft, exploration, unknown. Intent beats surface keywords on ambiguity.
• Target-model-aware prompt shaping — system prompt, maxTokens, and temperature adapt to the downstream LLM's context window and the resolved intent. Small local models get a compact prompt; Claude/GPT-4/Gemini get the full richness.
• Grounding Context (single, priority-ordered) — user pinned instructions → project rules → active file → prior accepted examples → web search → workspace metadata → target-model hints → custom platform instructions → built-in syntax hints. No more parallel context silos.
• Session retrieval (save_outcome) — the caller reports accepted | edited | rejected per optimization; similar accepted outputs in the same session get injected as few-shot examples into future similar prompts. Persistent memory lands in 1.3.
• Local JSONL tracing — every optimization writes a structured trace line (now with shape, groundingSources, error fields) to $CLARIFYPROMPT_HOME/traces/YYYY-MM-DD.jsonl. Nothing is uploaded. Toggle via CLARIFYPROMPT_TRACE=off.
• Unified $CLARIFYPROMPT_HOME — one env var for everything ClarifyPrompt writes. Legacy CLARIFYPROMPT_CONFIG_DIR / CLARIFYPROMPT_DATA_DIR still work (deprecation hint, silenceable).
• 58+ platforms, 7 categories, custom platforms — the original core is unchanged and fully backward-compatible.
• Any LLM, any provider. One code path works with any OpenAI-compatible API — Ollama (local + cloud), LM Studio, vLLM, OpenAI, Google Gemini, xAI Grok, Groq, Mistral, DeepSeek, Cohere, Perplexity, Together, Fireworks, OpenRouter — plus Anthropic Claude directly. Reasoning models (o1/o3/o4, deepseek-reasoner, gpt-oss, *-thinking) are auto-detected and given a larger token budget so they actually produce content. See 15+ pre-configured provider examples below.
• Apache-2.0, forever. Open-source core, no relicensing.

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"
      }
    }
  }
}

With AI Butler

AI Butler is a self-hosted personal AI agent runtime — single Go binary, multi-channel chat, MCP ecosystem hub. Drop ClarifyPrompt into its mcp.servers config and the agent picks up all 11 tools as native capabilities, callable from any channel (web chat, terminal, Telegram, Slack, etc.).

Edit ~/.aibutler/config.yaml:

configurations:
  mcp:
    servers:
      - name: clarifyprompt
        command: clarifyprompt-mcp
        env:
          LLM_API_URL: "http://localhost:11434/v1"
          LLM_MODEL: "qwen3-vl:8b"

Restart AI Butler. The boot log confirms all 11 tools are wired in:

The agent enumerates the full surface on request — every tool prefixed with clarifyprompt.:

Drive the Context Engine end-to-end

You can preview what the engine would gather (without running the optimization) using inspect_context:

Then run the actual optimizer for any of the 58+ supported platforms:

Every optimization gets a single JSONL line in ~/.clarifyprompt/traces/YYYY-MM-DD.jsonl — strictly local, never uploaded. The list_traces tool turns that into a queryable summary with replay support via get_trace:

The full integration walkthrough — including all 11 tools driven from chat, configuration options, and natural-language usage examples — is in the AI Butler docs: Integrate an MCP Server.

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.
session_id	No	Stitches related optimizations together so session memory can bias subsequent calls. Auto-generated when omitted.
file_path	No	Active file path — infers language and shapes platform hints.
file_language	No	Explicit language override for the active file.
file_excerpt	No	Short excerpt (≤2 KB) of the active file to ground the rewrite.
cwd	No	Working directory to scan for CLAUDE.md / AGENTS.md / .cursorrules / package.json. Defaults to server cwd.
user_locale	No	Locale hint (e.g. en-US, ar-EG) to inform tone and language.
user_pinned_instructions	No	Pinned, always-applied user instructions (short core-memory block).
include_bundle	No	Include the resolved ContextBundle summary in the response. Default: false.
skip_intent_resolution	No	Skip the intent classifier LLM call (faster; loses intent signal). Default: false.

Response (1.2.0):

{
  "id": "opt_mo9vlg9i_foohjx",
  "sessionId": "sess_mo9vlfn3_abc123",
  "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",
  "modeSource": "analyzer",
  "analysis": {
    "category": "image",
    "intent": "creative-media",
    "recommendedMode": "detailed",
    "confidence": "high",
    "source": "llm"
  },
  "grounding": {
    "sources": ["project-rules", "workspace-meta", "target-model", "platform-hints"],
    "acceptedExamplesUsed": 0
  },
  "shape": {
    "systemPromptBudget": "standard",
    "maxTokens": 2048,
    "temperature": 0.9
  },
  "metadata": {
    "model": "qwen2.5:14b-instruct-q4_K_M",
    "processingTimeMs": 3911,
    "strategy": "ImageStrategy"
  },
  "detection": { "autoDetected": true, "detectedCategory": "image", "detectedPlatform": "midjourney", "confidence": "high" },
  "intent": { "detected": "creative-media", "confidence": "high" }
}

The canonical classification field is analysis. The detection and intent fields are deprecated aliases kept for 1.x back-compat; they will be removed in 2.x.

modeSource tells you how the final mode was decided (user if you passed one, analyzer if intent-driven, default if neither).

grounding.sources lists which Grounding Context sections contributed, in priority order. grounding.acceptedExamplesUsed tells you how many few-shot examples the engine pulled from save_outcome history.

shape tells you how the system prompt was sized for your target model.

inspect_context (new in 1.2.0)

Preview the ContextBundle ClarifyPrompt would assemble for a given prompt — workspace rules, frameworks, target-model capabilities, resolved intent, and session history — without running the full optimization. Useful for debugging why an optimization turned out the way it did.

{
  "prompt": "Write an email to finance explaining the Q2 spend variance",
  "category": "document",
  "cwd": "/path/to/your/project"
}

Returns the full ContextBundle as JSON.

list_traces (new in 1.2.0)

Summary list of recent optimization traces captured by the local tracer (when CLARIFYPROMPT_TRACE=local, the default).

{ "day": "2026-04-22", "limit": 50 }

Returns trace IDs, inputs previews, resolved intents, target families, and latencies — never the full system prompt (use get_trace for that). Omit day to get the most recent day with data.

get_trace (new in 1.2.0)

Fetch the full trace for a single optimization by ID, including the exact system prompt, bundle summary, and output.

{ "id": "opt_xxx", "lookback_days": 7 }

save_outcome (new in 1.2.0)

Tell ClarifyPrompt whether a past optimization was accepted, edited, or rejected. Accepted outputs become few-shot examples for similar future prompts in the same session. In 1.3+ this will also feed the persistent memory layer. The IDE / agent / caller is expected to invoke this after the user acts on the optimization.

{
  "optimization_id": "opt_xxx",
  "session_id": "sess_yyy",
  "verdict": "accepted",
  "diff": "optional: the user's edited version or a patch"
}

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
CLARIFYPROMPT_HOME	No	Canonical (1.2.0+) root for everything ClarifyPrompt writes — custom platforms, instruction .md files, traces, and (1.3+) memory + packs. Default: $XDG_DATA_HOME/clarifyprompt or ~/.clarifyprompt.
CLARIFYPROMPT_TRACE	No	off | local | otel. Default: local. Traces are strictly local JSONL; nothing is uploaded.
CLARIFYPROMPT_SUPPRESS_LEGACY_WARN	No	Set to 1 to silence the one-line deprecation hint when CLARIFYPROMPT_CONFIG_DIR / CLARIFYPROMPT_DATA_DIR are used.
CLARIFYPROMPT_CONFIG_DIR	No	Legacy alias for CLARIFYPROMPT_HOME. Still works; will be removed in 2.x.
CLARIFYPROMPT_DATA_DIR	No	Legacy alias for CLARIFYPROMPT_HOME. Still works; will be removed in 2.x.

Provider Examples

Ollama (local, free):

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

Ollama — cloud models via local passthrough (recommended):

If your local Ollama is signed in to Ollama Cloud, any :cloud model routes through it transparently — same URL, no separate API key. The capability table auto-detects reasoning / thinking variants (gpt-oss, kimi-k2-thinking, qwen3-thinking, deepseek-r1, etc.) and bumps maxTokens so they finish thinking and actually produce content.

LLM_API_URL=http://localhost:11434/v1
LLM_MODEL=gpt-oss:20b-cloud        # or kimi-k2.6:cloud, qwen3-next:80b-cloud, glm-4.6:cloud, etc.

Ollama — direct cloud endpoint (no local install):

LLM_API_URL=https://ollama.com/v1
LLM_API_KEY=your-ollama-cloud-key
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]"

Context Engine (1.2.0)

Every optimization runs through five integrated passes that flow one bundle of context end-to-end:

1. Analysis — a single analyzePrompt() LLM call produces category, intent, and recommendedMode together so they can't disagree. Intent beats surface keywords when they conflict (e.g. "validate emails" → code not document).
2. Mode reconciliation — explicit user mode wins; otherwise the analyzer's intent-derived recommendation applies; modeSource in the response tells you which.
3. Prompt shaping — target-model capability signal drives systemPromptBudget (compact for small local models, rich for 100K+ ctx models), maxTokens, temperature (intent-aware), and whether examples are included.
4. Intent overlay — a short overlay per intent (production-code: demand error handling + tests; data-extract: demand strict schema; brand-voice: lead with tone; etc.) folded into the strategy's system prompt.
5. Grounding Context — a single priority-ordered block that merges user pinned instructions → project rules → active file → session few-shot examples → web search → workspace metadata → target-model hints → custom platform instructions → built-in syntax hints.

What's collected (ContextBundle)

• Project — first matching file from CLAUDE.md, AGENTS.md, .cursorrules, .clinerules, clarify.md, .clarify/rules.md. package.json plus sibling manifests (pyproject.toml, Cargo.toml, go.mod, Gemfile, composer.json, …) drive framework + language detection.
• File — optional file_path / file_language / file_excerpt inputs.
• Session — ring buffer (20 ops/session) of recent optimizations and outcomes. Accepted outputs get retrieved as few-shot examples for similar future prompts.
• Target model — the LLM doing the rewrite, matched against a capability table.
• User — locale, preferred mode, pinned instructions (highest-priority grounding).

Inspecting what the engine sees

Use the inspect_context tool to preview the full bundle without running an optimization. Same shape as optimize_prompt returns when include_bundle: true.

Extending context

Drop an AGENTS.md / clarify.md / CLAUDE.md at your project root. Next optimization picks it up automatically. To feed accepted outputs back into future rewrites, call save_outcome after the user acts on the result.

Tracing

$CLARIFYPROMPT_HOME/traces/YYYY-MM-DD.jsonl

Every optimization writes one JSONL line capturing {id, ts, sessionId, category, platform, mode, input, bundleSummary, systemPrompt, output, model, strategy, latencyMs, shape, groundingSources, error}. Use list_traces for summaries and get_trace for full records.

Privacy posture:

• Traces are strictly local. No outbound network calls to any ClarifyPrompt-owned infrastructure.
• Only calls out to the LLM endpoint you configured (LLM_API_URL) and optional search provider (SEARCH_API_KEY).
• Disable tracing entirely with CLARIFYPROMPT_TRACE=off.
• There is no telemetry in this release. When a telemetry option ships it will be opt-in, anonymous, and documented before the build includes it.

Known limitations & roadmap

Session memory is in-memory only (today)

The save_outcome + few-shot retrieval loop writes into a per-process ring buffer. Restarting the MCP server clears session state; two servers don't share memory. The MCP tool surface is deliberately stable — the interface won't change in 1.3. The upgrade is purely a backend swap to SQLite + sqlite-vec for disk persistence and richer similarity. Ship target: 1.3.

Intent quality scales with the model running the analyzer

The analyzer runs on the same LLM_MODEL that does the rewrite. In the integration battery:

• Qwen 2.5 7B and 14B → correct on every well-formed prompt tested.
• Llama 3.2 3B → occasionally over-commits on ambiguous prompts (e.g. tagged "make it better" as brand-voice/high when unknown/low is the right answer). Larger models on the same prompt correctly returned unknown/low.

Guidance: prefer a 7B+ local model (or any frontier hosted model) as LLM_MODEL. Latency-sensitive callers can set skip_intent_resolution: true to skip the analyzer; the engine falls back to user-hint category and default mode, losing intent-driven mode + overlay but keeping grounding + shape. A systematic eval harness with a public fixture set lands in 1.3 (Day 3) so you can score the analyzer against your own fixtures and detect regressions across model or classifier changes.

Capability table is not exhaustive

Entries today: Claude, GPT-4/o-series, Gemini, Grok, DeepSeek (chat + reasoning), Qwen, Llama, Mistral/Codestral, Mixtral, Gemma, Phi, Cohere Command, Aya, Kimi, GLM, Minimax, GPT-OSS, Yi, Nemotron. Unknown models fall back to capabilities: {} and standard prompt-shape — still functional, just without model-aware sizing. Adding entries is a data-only edit to src/engine/context/targetModelSignals.ts.

Reasoning / chain-of-thought models

Supported as a first-class case. The engine auto-detects reasoners at family level (o1/o3/o4, deepseek-reasoner, gpt-oss) and at variant level (anything whose ID matches /\b(thinking|reasoner|reasoning)\b/ or /\br[12]\b/: kimi-k2-thinking:cloud, qwen3-thinking:72b, qwen-r1-distill, etc.). For these, maxTokens is automatically bumped to ≥ 8192 so the model has room to think AND produce content. The reasoning field is never surfaced as the optimized prompt — only content is.

Architecture

clarifyprompt-mcp/
  src/
    index.ts                           MCP server entry point (11 tools, 1 resource)
    engine/
      config/
        categories.ts                  7 categories, 58 platforms, 7 modes
        paths.ts                       Unified $CLARIFYPROMPT_HOME resolver (1.2.0)
        persistence.ts                 ConfigStore — JSON config + .md file loading
        registry.ts                    PlatformRegistry — merges built-in + custom
      context/                         Context Engine (1.2.0)
        types.ts                       ContextBundle + signal types + AnalysisSignal
        projectSignals.ts              CLAUDE.md / AGENTS.md / .cursorrules / manifests scan
        fileSignals.ts                 Active-file path + language + excerpt
        sessionSignals.ts              In-memory per-session ring buffer + outcome retrieval
        targetModelSignals.ts          Model → capabilities mapping
        promptAnalyzer.ts              Unified analyzer: category + intent + recommendedMode
        bundle.ts                      Bundle orchestrator
      trace/                           Local tracing (1.2.0)
        types.ts                       TraceEntry schema (shape, groundingSources, error)
        writer.ts                      JSONL + OTel-stub writer, reader, lookup
      llm/client.ts                    Multi-provider LLM client (OpenAI + Anthropic)
      search/client.ts                 Web search (6 providers; results merge into Grounding Context)
      optimization/
        engine.ts                      Core orchestrator — analyzer, shape, grounding, retrieval, trace
        groundingContext.ts            Priority-ordered context assembly + mode/shape helpers
        types.ts                       OptimizationContext + result shape
        strategies/
          base.ts                      Bundle-aware base strategy (intent overlay + shape-aware sizing)
          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