LumabyteCo/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, GPT Image 2, Veo, Runway, Kling, Seedance, Higgsfield, ElevenLabs, Suno, Claude, ChatGPT, Cursor, or any of the 61 supported platforms — with the right syntax, parameters, structure, and grounding.
> **New in 1.16.0:** **the platform-currency release.** Every built-in pack fact-verified against current vendor docs (2026-10-05) — Midjourney V8.x, Runway Gen-4.5, Veo 3.1, Kling 3.0/Omni, Pika 2.2, Suno v6, ElevenLabs v3, gpt-4o-mini-tts, GPT Image 2 — each entry now carries a verifiedAt date. **Sora retired everywhere** (OpenAI shut the Videos API 2026-09-24, no replacement); new platforms: Seedance, ElevenLabs Music v2.5, Lyria 3.5. Plus the **dialect-currency benchmark**: 12 of the newest Ollama Cloud models asked to write platform prompts unprompted — 12/13 emit stale Midjourney flags, 12/12 write fluent prompts for dead Sora, zero hedge. Grounded mode re-runs the same asks with the pack facts injected and the scores flip. See the benchmark and CHANGELOG.md.
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> **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.
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> **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 badge and a clean **Your prompt → Optimized** before/after (with a show changes toggle) instead of an always-on diff. See CHANGELOG.md.
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> **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.
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> **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.
The proof this matters — measured, not asserted
Why does this project exist? **Because "just ask the model" quietly went stale, and we measured it.** Models are frozen at training time; platforms ship new versions monthly (and kill old ones). The result is that asking a modern model for a platform prompt is asking a 2024 encyclopedia for 2026 prices.
On 2026-10-05 we ran a public benchmark: asked **12 of the newest Ollama Cloud models** to write prompts for real current platforms, in **two conditions**, same asks, same models:
- **RAW** — the model alone, no help. What a user gets in a plain chat.
- **GROUNDED** — the same asks with the *verified platform facts* injected first — the same pack data ClarifyPrompt injects on every optimize_prompt call. (This is what "grounding" means: the model reads the reference card instead of trusting its frozen memory.)
132 graded answers per condition, verified against vendor docs the same day. **Full audit of every raw reply: evals/dialect-benchmark/out/final.json.**
**The results, one line:**