ClarifyPrompt MCP

A context-aware MCP prompt compiler that transforms vague prompts into platform-optimized prompts for 60+ 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, Higgsfield, ElevenLabs, Claude, ChatGPT, Cursor, or any of the 60+ supported platforms — with the right syntax, parameters, structure, and grounding.

New in 1.15.0: Nano Banana (Google Gemini 2.5 Flash Image) is now a built-in image platform — optimize_prompt(platform: "nano-banana") compiles image prompts in its native style (natural-language scene direction, photographic terms, edit-preserving-identity phrasing, in-image text). Plus latest-model compatibility across every provider: claude-sonnet-5, gpt-5/o-series, and Gemini reject temperature and/or max_tokens; the client now sends the right parameters (proactively for known reasoning ids, and learns the rest from a 400). Verified live against Anthropic, OpenAI, Gemini, and Ollama Cloud. See CHANGELOG.md.

New in 1.14.1: Portable-by-default text output — a chat/document/code prompt with no explicit platform now stays platform-neutral instead of quietly defaulting to Claude's idioms (XML tags); name a platform to opt into vendor-specific tuning. Plus the MCP Apps compose panel now shows a for <platform> badge and a clean Your prompt → Optimized before/after (with a show changes toggle) instead of an always-on diff. See CHANGELOG.md.

New in 1.14.0: An interactive compose panel via MCP Apps. In hosts that speak the io.modelcontextprotocol/ui extension (Claude Desktop, ChatGPT, Cursor, VS Code, …), compose_prompt renders a live panel: original-vs-optimized view, all six critique scores, the pipeline stages, and Accept / Revise actions — Accept records the outcome into ClarifyPrompt's memory loop, Revise sends your feedback back into the chat. One self-contained ui:// resource; hosts without the extension see zero change. See CHANGELOG.md.

New in 1.13.0: Plain-language rewrites. Optimized prompts now stick to common, everyday words instead of drifting into formal vocabulary ("use", never "utilize") — specificity comes from concrete details, not fancier synonyms. critique_prompt gained a 6th default dimension, plain_language, so auto_revise loops correct register drift automatically. Also fixed: an explicit mode (e.g. simple) is no longer silently dropped for small local models under compact system-prompt shaping. See CHANGELOG.md.

How It Works

ClarifyPrompt does two things a plain prompt template can't. Every output below is a real, unedited capture from optimize_prompt run against this repo (see Provenance at the end of this section).

1 — It knows each platform. Same raw prompt, different target, completely different output:

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

→ Midjourney  A colossal, majestic dragon with shimmering scales soaring over a towering
              medieval stone castle, dramatic sunset sky with vibrant orange and deep purple
              hues, cinematic fantasy concept art, volumetric lighting, highly detailed
              --ar 16:9 --v 6.1 --s 250 --q 2

→ DALL-E      A majestic dragon with glowing crimson scales soars over a towering medieval
              stone castle, silhouetted against a vibrant orange and purple sunset sky.
              Rendered in a high-fantasy digital art style with dramatic, warm lighting and
              highly detailed textures, wide aspect ratio.

→ Nano Banana A majestic dragon with deep crimson scales and a leathery, bat-like wingspan
              glides through the warm, golden-hour sky just above a towering medieval castle
              made of weathered grey stone. ... Frame this as a wide cinematic landscape shot
              using a 24mm lens at f/8 for deep depth of field, camera positioned at a
              slightly elevated three-quarter angle... Aspect ratio 16:9.

Midjourney gets --ar/--v/--s/--q flags; DALL-E and Nano Banana get flag-free natural language — and Nano Banana layers in photographic direction (lens, f-stop, camera angle) and explicit mood, its documented style. Same idea, each platform's native dialect.

2 — It knows what you're working on. This is the part a template can't fake. Drop a vague one-liner while editing src/transport.ts in this very repo, and the engine grounds it in your real workspace — package.json, git state, the active file — and resolves intent before it shapes the output:

You write:    "add a configurable request timeout to the http transport"
              · active file: src/transport.ts   · resolved intent: production-code
              · grounded in: active-file · workspace-meta · git-state · environment ·
                target-model · platform-hints

→ Cursor      Implement a configurable request timeout for the HTTP transport in
              `src/transport.ts`.
              Requirements:
              1. Add a new environment variable `CLARIFYPROMPT_HTTP_TIMEOUT` … (default 30000 ms)
              2. Apply this timeout to all incoming requests in the streamable-http transport
              …
              5. Preserve existing behavior for stdio and a2a transports
              …
              The implementation should be added to the streamable-http section of
              `startTransport()`.
              (excerpted — the full rewrite has 7 numbered requirement groups)

Nothing in that one-line prompt mentioned the CLARIFYPROMPT_HTTP_* naming convention, the startTransport() entry point, or the stdio/a2a transports it must preserve — the engine read those from the active file and package.json and folded them in. That's the difference between rephrasing a prompt and compiling it against context.

3 — It can run the whole pipeline. clarify → ground/optimize → critique → revise, in one compose_prompt call — see Previously in 1.4.0 — the composable pipeline below.

Provenance. Image outputs captured via glm-5.2:cloud, the grounded code output via qwen3-coder:480b-cloud — both Ollama cloud models served over Ollama's OpenAI-compatible endpoint (LLM_API_URL=http://localhost:11434/v1), run through optimize_prompt against this repo on 2026-06-22 (the Nano Banana capture added 2026-07-03, same glm-5.2:cloud model). ClarifyPrompt is model-agnostic (any OpenAI-compatible API, local or hosted); outputs are model-dependent — yours will differ in wording, not in structure.

What's new in 1.15.0

Nano Banana, built in. Google's Gemini 2.5 Flash Image ("Nano Banana") is now a first-class image platform — optimize_prompt(category: "image", platform: "nano-banana") compiles your idea into its native prompting style: full-sentence scene direction (not keyword piles), photographic terminology for camera/lens/depth, explicit lighting, edit-phrasing that preserves subject identity, multi-reference character consistency, and reliable in-image text. Like every image platform, ClarifyPrompt compiles the prompt; you send it to the model.

Latest-model compatibility, every provider. Thinking-enabled models reject parameters clarifyprompt always sent: claude-sonnet-5 and OpenAI reasoning models reject temperature; gpt-5 / o-series also reject max_tokens (they require max_completion_tokens). Every call to them used to fail and degrade to the original prompt. Now the client sends the right body — proactively for well-known reasoning ids (no wasted round-trip) and, for anything the hints don't recognize (including future models), it learns from the 400 and retries. Models that accept the standard parameters are byte-identical. Verified live against Anthropic (claude-sonnet-5), OpenAI (gpt-5), Gemini (gemini-flash-latest), and Ollama Cloud (glm-5.2:cloud). Reasoning models think a lot — bump LLM_TIMEOUT_MS (the 30s default is often too short).

What's new in 1.14.1

Portable by default. When you optimize a text prompt (chat, document, code) without naming a platform, ClarifyPrompt now returns platform-neutral output — clean, portable structure that works in any assistant — instead of quietly defaulting to Claude's idioms (its <task>/<context> XML tags). Name a platform (platform: "claude", "chatgpt", … any of the 60) to opt into that platform's specific tuning. Creative categories (image/video/voice/music) are unchanged: their output needs a concrete platform format, so the flagship default (Midjourney, Runway, …) still applies.