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New features are opt-in via environment variables.","backend_compatibility":"Fully aligned with FastAPI Backend v2.1.0-bayesian"}},"support":{"email":"support@promptoptimizer.xyz","documentation":"https://promptoptimizer.xyz/docs","dashboard":"https://promptoptimizer.xyz/dashboard","pricing":"https://promptoptimizer.xyz/pricing"},"_id":"mcp-prompt-optimizer@3.8.3","gitHead":"88892ff566ab813e8a2de33c90df900798ac74b6","_nodeVersion":"22.12.0","_npmVersion":"11.4.1","dist":{"integrity":"sha512-O6QZjCOE1RG9JdRl/OI7SYHbgYnr4sk3zyUGh7KttNrKH1q+tznlZI42rz1u5IVQYjQdr5e0mTxPdt4T172sdA==","shasum":"761c62c94a79f658e0b1e9f6e93676a4077d38ee","tarball":"https://registry.npmjs.org/mcp-prompt-optimizer/-/mcp-prompt-optimizer-3.8.3.tgz","fileCount":14,"unpackedSize":231158,"signatures":[{"keyid":"SHA256:DhQ8wR5APBvFHLF/+Tc+AYvPOdTpcIDqOhxsBHRwC7U","sig":"MEUCIFigQFhbUjZTE28jsqb3rDtxljKxJIV3+NDVGVhq6MSpAiEA8CDjuaker/4mWK6dPb7MBAor2N3paQuP/W0EQzbCtzs="}]},"_npmUser":{"name":"nivlewd","email":"nivlewd@yahoo.com"},"directories":{},"maintainers":[{"name":"nivlewd","email":"nivlewd@yahoo.com"}],"_npmOperationalInternal":{"host":"s3://npm-registry-packages-npm-production","tmp":"tmp/mcp-prompt-optimizer_3.8.3_1788808494434_0.988776190622636"},"_hasShrinkwrap":false}},"time":{"created":"2025-06-12T02:45:35.285Z","modified":"2026-09-07T19:14:54.710Z","1.0.1":"2025-06-12T02:45:35.483Z","1.0.2":"2025-06-18T16:33:08.075Z","1.0.3":"2025-06-18T17:02:01.958Z","1.1.0":"2025-06-21T09:18:53.741Z","1.1.1":"2025-07-08T09:24:16.913Z","1.3.2":"2025-07-12T20:37:18.628Z","1.3.3":"2025-07-19T07:01:35.768Z","1.4.0":"2025-07-28T22:33:15.503Z","1.4.2":"2025-09-18T01:31:06.943Z","1.5.0":"2025-09-25T19:44:43.157Z","2.2.3":"2025-11-01T08:17:09.247Z","3.0.1":"2025-12-10T01:27:36.011Z","3.0.2":"2026-03-06T05:00:05.226Z","3.0.3":"2026-03-10T04:09:25.612Z","3.0.4":"2026-03-10T06:50:22.528Z","3.0.5":"2026-03-13T09:26:11.572Z","3.1.0":"2026-04-09T18:21:00.734Z","3.1.1":"2026-04-11T00:21:30.787Z","3.1.2":"2026-04-13T06:18:04.554Z","3.1.3":"2026-04-13T06:49:58.111Z","3.2.0":"2026-05-16T07:15:42.790Z","3.2.1":"2026-05-20T02:44:08.862Z","3.2.2":"2026-05-20T02:45:31.020Z","3.2.3":"2026-05-22T06:13:09.182Z","3.3.0":"2026-05-24T03:34:08.914Z","3.4.0":"2026-05-26T15:39:32.628Z","3.4.1":"2026-06-01T19:05:46.386Z","3.5.0":"2026-06-06T02:50:23.183Z","3.6.0":"2026-06-11T18:58:02.265Z","3.7.0":"2026-06-18T04:00:28.500Z","3.7.1":"2026-06-26T10:07:54.823Z","3.7.2":"2026-07-05T23:39:57.537Z","3.7.3":"2026-07-16T19:37:22.156Z","3.7.4":"2026-07-18T03:01:44.744Z","3.7.5":"2026-07-18T17:29:17.850Z","3.8.1":"2026-07-30T03:29:14.670Z","3.8.2":"2026-08-28T04:18:53.939Z","3.8.3":"2026-09-07T19:14:54.565Z"},"bugs":{"url":"https://github.com/prompt-optimizer/mcp-prompt-optimizer/issues"},"author":{"name":"Prompt Optimizer Team","email":"support@promptoptimizer.xyz"},"license":"Proprietary","homepage":"https://promptoptimizer.xyz","keywords":["mcp","mcp-server","prompt","optimization","ai","claude","anthropic","prompt-engineering","llm","cursor","windsurf","cline","context-aware","professional","automation"],"repository":{"type":"git","url":"git+https://github.com/prompt-optimizer/mcp-prompt-optimizer.git"},"description":"Professional cloud-based MCP server for AI-powered prompt optimization with intelligent context detection, Bayesian optimization, AG-UI real-time optimization, template auto-save, optimization insights, personal model configuration via WebUI, team collabo","maintainers":[{"name":"nivlewd","email":"nivlewd@yahoo.com"}],"readme":"# MCP Prompt Optimizer\n\n[![NPM Version](https://img.shields.io/npm/v/mcp-prompt-optimizer)](https://www.npmjs.com/package/mcp-prompt-optimizer)\n[![License](https://img.shields.io/badge/license-Commercial-blue.svg)](LICENSE)\n[![CI](https://github.com/prompt-optimizer/mcp-prompt-optimizer/actions/workflows/ci.yml/badge.svg)](https://github.com/prompt-optimizer/mcp-prompt-optimizer/actions)\n[![Snyk Health](https://snyk.io/advisor/npm-package/mcp-prompt-optimizer/badge.svg)](https://snyk.io/advisor/npm-package/mcp-prompt-optimizer)\n\n🚀 **Professional cloud-based MCP server** for AI-powered prompt optimization with intelligent context detection, template management, team collaboration, and enterprise-grade reliability. Starting at $0/month.\n\n\n\n## ✨ Key Features\n\n🧠 **AI Context Detection** — Automatically detects and optimizes for code, creative writing, image generation, communication, and more\n📁 **Template Management** — Auto-save high-confidence optimizations, search & reuse patterns\n👥 **Team Collaboration** — Shared quotas, team templates, role-based access\n📊 **Confidence Scoring** — Honest quality signal with per-tier annotations\n☁️ **Cloud Processing** — Always up-to-date AI models via backend LLM pipeline\n🔧 **Resilient Fallback** — Structured local optimization if the backend is unreachable\n🎛️ **Personal Model Choice** — Use your own OpenRouter models via WebUI configuration\n🔧 **Universal MCP** — Works with Claude Desktop, Cursor, Windsurf, Cline, VS Code, Zed, Replit\n\n---\n\n## 🚀 Quick Start\n\n**1. Get your API key:**\n\n- **🆓 Free Tier** (`sk-opt-*`): 20 LLM optimizations/month, 1 API key — no credit card required\n- **⭐ Pro** (`sk-opt-*`): 500 optimizations/month, full model config, Context Engineer\n- **🏢 Enterprise** (`sk-team-*`): Unlimited optimizations, team keys, shared quotas\n\nSign up and generate your key at [promptoptimizer.xyz/dashboard](https://promptoptimizer.xyz/dashboard).\n\n**2. Install:**\n```bash\nnpm install -g mcp-prompt-optimizer\n```\n\n**3. Connect to Claude Desktop (one command):**\n```bash\nnpx mcp-prompt-optimizer connect\n```\nThis wizard reads your API key and writes the MCP server entry to Claude Desktop config automatically. Cross-platform (macOS, Windows, Linux).\n\n**Or configure manually** — add to `~/.claude/claude_desktop_config.json`:\n```json\n{\n  \"mcpServers\": {\n    \"mcp-prompt-optimizer\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"mcp-prompt-optimizer\"],\n      \"env\": {\n        \"OPTIMIZER_API_KEY\": \"sk-opt-your-key-here\"\n      }\n    }\n  }\n}\n```\n\n**4. Restart your MCP client** and start optimizing.\n\n> **Note:** API keys are validated against the backend server. An internet connection is required (responses are cached for up to 2 hours for reliability).\n\n---\n\n## ⚙️ How Optimization Works\n\nPrompts are routed through a three-tier pipeline based on complexity and context. Each tier produces a different output format and confidence range.\n\n### Tier 1 — LLM Optimization (backend, confidence 70–95%)\n\nThe backend routes complex or high-sophistication prompts through a Gemini Flash LLM pass that genuinely rewrites and enriches the prompt. This is the highest-quality output.\n\n```\n# 🎯 Optimized Prompt\n\nMy Python script crashes with a KeyError on line 47. I need help diagnosing\nthe root cause. I am using Python 3.11 with pandas 2.0. The error occurs\nwhen accessing dictionary keys after a merge operation.\n\n**Confidence:** 82.0%\n**AI Context:** code_generation\n```\n\n### Tier 2 — Backend Rules Optimization (backend, confidence < 25%)\n\nFor simpler or lower-sophistication prompts the backend applies rules-based optimization without an LLM. When this happens, confidence will be below 25% and a note will appear explaining what to check if you expected full LLM enhancement.\n\n```\n# 🎯 Optimized Prompt\n\nMy Python script crashes with a KeyError. Please provide the error message\nand relevant code.\n\n> ℹ️ *Low confidence indicates the backend applied rules-based optimization\n> (no LLM). Ensure `OPENROUTER_API_KEY` is configured in the backend for\n> full LLM enhancement.*\n\n**Confidence:** 18.0%\n**AI Context:** code_generation\n```\n\n### Tier 3 — Local Rules Fallback (npm, confidence 35–55%)\n\nIf the backend is unreachable, the npm package applies optimization locally using a library of domain-specific templates. The output is a structured, user-facing prose prompt — no XML scaffolding. The confidence annotation makes the quality tier explicit.\n\n```\n# 🔧 Rules-Based Optimization Applied\n\n⚠️ *Backend unreachable — your prompt has been structured using local rule\ntemplates (no LLM). Re-run when the backend is available for full LLM\noptimization.*\n\n**Template:** `debugging_request`\n\n**Optimized Prompt:**\n```\nMy Python script crashes with a KeyError\n\nTo address this effectively:\n- Identify the most likely root causes given the error description.\n- Request the full error message and stack trace, plus the relevant code snippet.\n- Ask about expected vs. actual behavior, programming language, and library versions.\n\n*Response format: Step-by-step debugging walkthrough with a specific fix or next diagnostic steps.*\n```\n\n**Confidence:** 51.0% *(rules-based — LLM optimization typically 70–95%)*\n**AI Context:** code_generation\n```\n\n---\n\n## 🧠 AI Context Detection\n\nThe server automatically detects your prompt type and routes it to the appropriate optimization template.\n\n| Context | `ai_context` value | Example patterns |\n|---|---|---|\n| Code & debugging | `code_generation` | `debug`, `fix`, `error`, `python`, `sql`, `refactor` |\n| Web development | `code_generation` | `landing page`, `react`, `css`, `html`, `tailwind` |\n| API & integration | `api_automation` | `api`, `rest`, `endpoint`, `oauth`, `webhook` |\n| DevOps & automation | `technical_automation` | `deploy`, `docker`, `ci/cd`, `terraform`, `bash` |\n| Data & schemas | `structured_output` | `json`, `schema`, `csv`, `yaml`, `transform` |\n| Analysis & research | `llm_interaction` | `analyze`, `explain`, `compare`, `research` |\n| Creative writing | `creative_writing` | `story`, `poem`, `script`, `blog`, `copywriting` |\n| Email & communication | `human_communication` | `email`, `letter`, `memo`, `formal`, `reply` |\n| Image generation | `image_generation` | `photorealistic`, `midjourney`, `dall-e`, `portrait` |\n| Business & Strategy | `business_strategy` | `market analysis`, `swot`, `business plan` |\n| Technical Strategy | `technical_strategy` | `architecture`, `system design`, `migration plan` |\n| Legal & Compliance | `legal_compliance` | `contract`, `gdpr`, `terms of service`, `audit` |\n| Medical & Health | `medical_healthcare` | `clinical`, `diagnosis`, `treatment`, `hipaa` |\n| Education | `educational_content` | `lesson plan`, `syllabus`, `curriculum`, `quiz` |\n| General | `general_assistant` | Everything else |\n\n### Image Generation\n\nImage prompts are handled separately. The local fallback appends style-matched quality boosters directly to the prompt (comma-separated), rather than producing structured bullets. This matches how image generation models consume prompts.\n\n```\nInput:  \"Draw a photorealistic portrait of an astronaut\"\nOutput: \"Draw a photorealistic portrait of an astronaut, ultra realistic,\n         sharp focus, professional photography, dynamic lighting, balanced\n         composition, high quality, 4K\"\n```\n\n---\n\n## 🛠️ Available MCP Tools\n\n### `optimize_prompt`\nAI optimization with context detection, auto-save, and insights.\n```json\n{\n  \"prompt\": \"Your prompt text\",\n  \"goals\": [\"clarity\", \"specificity\"],\n  \"ai_context\": \"code_generation\",\n  \"enable_bayesian\": true\n}\n```\n\n### `detect_ai_context`\nDetect the AI context for a given prompt.\n```json\n{\n  \"prompt\": \"The prompt text for which to detect the AI context\"\n}\n```\n\n### `create_template`\nSave an optimization as a reusable template.\n```json\n{\n  \"title\": \"Template title\",\n  \"description\": \"Optional description\",\n  \"original_prompt\": \"The original prompt\",\n  \"optimized_prompt\": \"The optimized prompt\",\n  \"optimization_goals\": [\"clarity\"],\n  \"confidence_score\": 0.9,\n  \"ai_context_detected\": \"code_generation\",\n  \"is_public\": false,\n  \"tags\": [\"debugging\", \"python\"]\n}\n```\n\n### `get_template`\nRetrieve a saved template by ID.\n```json\n{\n  \"template_id\": \"the-template-id\"\n}\n```\n\n### `update_template`\nUpdate an existing template.\n```json\n{\n  \"template_id\": \"the-template-id\",\n  \"title\": \"Updated title\",\n  \"is_public\": true\n}\n```\n\n### `search_templates`\nSearch your saved template library.\n```json\n{\n  \"query\": \"debugging\",\n  \"ai_context\": \"code_generation\",\n  \"limit\": 5,\n  \"sort_by\": \"confidence_score\",\n  \"sort_order\": \"desc\"\n}\n```\n\n### `get_quota_status`\nCheck subscription status and quota usage. No parameters required.\n\n### `get_optimization_insights` *(conditional)*\nBayesian optimization insights and parameter tuning recommendations. Requires the feature to be enabled in the backend; returns mock data otherwise.\n```json\n{\n  \"analysis_depth\": \"detailed\",\n  \"include_recommendations\": true\n}\n```\n\n### `get_real_time_status` *(conditional)*\nReal-time optimization status and AG-UI capabilities. Requires the feature to be enabled in the backend; returns mock data otherwise. No parameters required.\n\n---\n\n## 🤖 Context Engineer (CE) Tools\n\nRequires a Pro or Enterprise subscription. CE tools generate agentic scaffolding artifacts (SOPs, skill packages, framework code) directly in your IDE.\n\n### `generate_agent_sop`\nGenerate a structured SOP document for an AI agent from a goal description. Returns markdown SOP ready for use.\n```json\n{\n  \"goal\": \"What the agent should accomplish\",\n  \"context\": \"Additional context, constraints, or domain info (optional)\",\n  \"model_id\": \"Model to use (optional)\"\n}\n```\n\n### `generate_skill_package`\nGenerate a complete skill package (SOP + SKILL.md + reference + examples + helper.py) for a given agent goal. Takes 30–90 seconds (async with polling).\n```json\n{\n  \"goal\": \"What the agent should accomplish\",\n  \"format\": \"knowledge_doc\",\n  \"model_id\": \"Model to use (optional)\"\n}\n```\n`format` is one of `knowledge_doc` (default) or `agent_spec`.\n\n### `generate_harness_bundle`\nGenerate a deployment-ready Agentic Harness ZIP bundle for a specific platform. Returns a confirmation when the bundle is queued for download.\n```json\n{\n  \"goal\": \"The workflow goal the harness is built for\",\n  \"deploy_target\": \"claude_code\",\n  \"sop_content\": \"The SOP markdown content (required if no session_id)\",\n  \"session_id\": \"Optional: session ID from a prior generate_skill_package call\"\n}\n```\n`deploy_target` accepts a single string (Pro+) or an array of strings (Enterprise — multi-platform simultaneously). Supported targets include `claude_code`, `langchain`, `autogen`, `crewai`, `amazon_q`, `aider`, `continue_dev`, and more. `amazon_q`, `aider`, `continue_dev`, `crewai` require Enterprise.\n\n### `explore_sop_approaches`\nGenerate 3 parallel SOP variants (process-oriented, decision-tree, role-based) for comparison before committing to one. Returns an HTML comparison grid, a `variants` array, and a recommended variant. Enterprise required.\n```json\n{\n  \"goal\": \"The workflow goal to generate SOP variants for\",\n  \"context\": \"Optional background context\",\n  \"blend_description\": \"Optional: blend all 3 variants into one SOP using this description\"\n}\n```\nIntentFrame fields (`perspective`, `out_of_scope`, `success_definition`) are also accepted to scope the generation.\n\n### `transform_for_framework`\nTransform a SOP into native code for your agent framework: LangChain tool, AutoGen agent, or Claude Code skill.\n```json\n{\n  \"sop_content\": \"The SOP markdown content\",\n  \"goal\": \"What the agent should accomplish\",\n  \"framework\": \"langchain_tool\"\n}\n```\n`framework` is one of `langchain_tool`, `autogen_agent`, or `claude_skill`.\n\n### `get_ce_quota_status`\nCheck your Context Engineer credit balance and what workflows are available at your tier. No parameters required.\n\n---\n\n## 🎛️ Advanced Model Configuration (Optional)\n\nConfigure custom models in the WebUI and the MCP server uses them automatically.\n\n**Step 1 — Configure in WebUI:**\n1. Visit [Dashboard](https://promptoptimizer.xyz/dashboard)\n2. Go to Settings → User Settings\n3. Add your OpenRouter API key (from [openrouter.ai](https://openrouter.ai))\n4. Select your preferred models for optimization and evaluation\n\n**Step 2 — Use the npm package as normal.** Your WebUI model settings are applied automatically — no changes to the MCP configuration required.\n\n### Model Selection Priority\n```\n1. Your WebUI-configured models  (highest priority)\n2. Request-specific model override\n3. System default (google/gemini-flash-1.5-8b)\n```\n\n### Example Model Recommendations\n\n| Use case | Optimization model | Evaluation model |\n|---|---|---|\n| Creative / complex | `anthropic/claude-3-5-sonnet` | `google/gemini-pro-1.5` |\n| Fast / simple | `openai/gpt-4o-mini` | `openai/gpt-3.5-turbo` |\n| Code / technical | `anthropic/claude-3-5-sonnet` | `anthropic/claude-3-haiku` |\n\n> **Two different API keys:**\n> - **Service key** (`sk-opt-*` or `sk-team-*`) — your MCP Prompt Optimizer subscription\n> - **OpenRouter key** — your personal OpenRouter account for model usage costs\n\n---\n\n## 💰 Subscription Plans\n\n| Plan | Price | Optimizations/month | CE Credits | API Keys |\n|---|---|---|---|---|\n| 🆓 Free | $0 | 20 LLM | — | 1 |\n| ⭐ Pro | $19/mo | 500 | 5 | 1 |\n| 🏢 Enterprise | Custom | Unlimited | 50 | 10 (shared) |\n\nAll plans include AI context detection, template management, personal model configuration, and optimization insights.\n\n[Get started free →](https://promptoptimizer.xyz/dashboard)\n\n---\n\n## 🔧 CLI Commands\n\n```bash\nnpx mcp-prompt-optimizer connect        # Interactive wizard: add API key to Claude Desktop config\nmcp-prompt-optimizer check-status       # Check API key and quota status\nmcp-prompt-optimizer validate-key       # Validate API key with backend\nmcp-prompt-optimizer diagnose           # Run comprehensive diagnostic\nmcp-prompt-optimizer clear-cache        # Clear validation cache\nmcp-prompt-optimizer help               # Show help and setup instructions\nmcp-prompt-optimizer version            # Show version information\n```\n\n---\n\n## 🏢 Team Collaboration\n\n### Team API Keys (`sk-team-*`)\n- Shared quotas across team members\n- Centralized billing and management\n- Team template libraries for consistency\n- Role-based access control\n\n### Individual API Keys (`sk-opt-*`)\n- Personal quotas and billing — available on Free and Pro tiers\n- Individual template libraries\n- Account self-management\n\n---\n\n## 🔐 Security & Privacy\n\n- Encrypted data transmission\n- API key validation with secure backend authentication\n- Quota enforcement with real-time usage tracking\n- No prompt data retained — processed and discarded immediately\n- GDPR compliant\n\n---\n\n## 🔧 Universal MCP Client Support\n\n### Claude Desktop\n```json\n{\n  \"mcpServers\": {\n    \"mcp-prompt-optimizer\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"mcp-prompt-optimizer\"],\n      \"env\": { \"OPTIMIZER_API_KEY\": \"sk-opt-your-key-here\" }\n    }\n  }\n}\n```\n\n### Cursor IDE\nAdd to `~/.cursor/mcp.json`:\n```json\n{\n  \"mcpServers\": {\n    \"mcp-prompt-optimizer\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"mcp-prompt-optimizer\"],\n      \"env\": { \"OPTIMIZER_API_KEY\": \"sk-opt-your-key-here\" }\n    }\n  }\n}\n```\n\n### Other clients\nWindsurf, Cline, VS Code, Zed, Replit, JetBrains IDEs, and Neovim are all supported via standard MCP server configuration.\n\n---\n\n## 📦 Changelog\n\n### v3.7.2\n- **Stdout / JSON-RPC safety** — All MCP server startup logs (banner, status, mode indicators, key validation) now route to **stderr** instead of stdout. On stdio transports stdout is the JSON-RPC channel; previously any non-protocol writes there risk corrupting the stream. No user-facing change beyond logging.\n- **Contract test** — `npm run test:contract` pins the `/optimize` response shape (Pydantic fields + runtime-injected `quota_used`/`quota_limit` + metadata `routing_score`/`routing_tier`/`model_used`) against a canonical fixture. Catches backend drift before publish.\n- **CI workflow** — `.github/workflows/ci.yml` runs `npm ci` + health check + contract check on push/PR across Node 18, 20, and 22.\n\n### v3.6.0\n- **Free tier MCP access** — Free users can now create an API key (`sk-opt-*`) and use the MCP server at no cost (currently 20 LLM optimizations/month). Previously, key creation was blocked in the live service due to a bug introduced when the free-tier launch landed in an unused module. Keys with `subscription_status: NULL` (all free accounts) now validate correctly.\n- **`connect` wizard** — `npx mcp-prompt-optimizer connect` interactively writes your API key to Claude Desktop config on macOS, Windows, and Linux. Replaces manual JSON editing for new users.\n- **Upsell block** — Local rules fallback output now includes a concise upgrade prompt with a one-command onboarding path when no API key is configured.\n\n### v3.5.0\n- LLM upsell block added to local fallback output\n- `connect` subcommand added (interactive Claude Desktop config wizard)\n\n### v3.4.1\n- Tier names migrated from Explorer/Creator/Innovator to Free/Pro/Enterprise (D6 pricing)\n\n### v3.4.0\n- `explore_sop_approaches` tool: generates 3 parallel SOP variants (process-oriented, decision-tree, role-based) for comparison before committing. Optionally accepts `blend_description` to blend variants directly. Enterprise tier required.\n\n### v3.3.0\n- `generate_harness_bundle` tool: generates deployment-ready ZIP harness for 14+ platforms. Single deploy target (Pro+) or multi-platform array (Enterprise).\n- Intent frame fields (`perspective`, `out_of_scope`, `success_definition`) added to `generate_agent_sop` for scoped SOP generation.\n\n### v3.2.x\n- `transform_for_framework` tool: converts SOP to native LangChain tool, AutoGen agent, or Claude Code skill\n- CE harness HTML review layer with interactive DAG visualization\n\n### v3.0.3\n- **Rules fallback output rewritten** — local optimization now produces user-facing prose prompts instead of raw XML scaffolding. The output is directly usable as a prompt without modification.\n- **All 18 local templates reworded** — template principles are now phrased as user request guidance rather than AI-assistant directives, producing more natural and actionable structured prompts.\n- **`creative_writing` context** now routes correctly to the creative writing template (previously fell through to a generic fallback).\n- **`general_assistant` context** now maps explicitly to the LLM interaction template.\n- **Backend rules-tier detection** — when the backend returns confidence below 25% (indicating it ran its own rules tier without an LLM), a note appears explaining the cause and how to resolve it (`OPENROUTER_API_KEY` configuration).\n- **Confidence scale annotation** — rules-based fallback confidence now shows `*(rules-based — LLM optimization typically 70–95%)*` so users understand where their result sits relative to full LLM optimization.\n\n### v3.0.2\n- Cross-platform binary compatibility improvements\n- Bayesian optimization integration\n- AG-UI feature flag support\n\n### v3.0.0\n- API key now required for all operations\n- Development mode and offline mode removed for security\n- All keys validated against backend server\n\n---\n\n## 🛠️ Support & Resources\n\n- 📖 **Documentation:** [promptoptimizer.xyz/docs](https://promptoptimizer.xyz/docs)\n- 📊 **Dashboard & model config:** [promptoptimizer.xyz/dashboard](https://promptoptimizer.xyz/dashboard)\n- 🚀 **Pricing & API Keys**: [promptoptimizer.xyz/pricing](https://promptoptimizer.xyz/pricing)\n- 🐛 **Issues:** [GitHub Issues](https://github.com/prompt-optimizer/mcp-prompt-optimizer/issues)\n- 📄 **License:** [Commercial License](LICENSE)\n- 🔒 **Security:** [Security Policy](SECURITY.md)\n- 🤝 **Contributing:** [Contributing Guide](CONTRIBUTING.md)\n- 📜 **Code of Conduct:** [Contributor Covenant](CODE_OF_CONDUCT.md)\n- 📝 **Changelog:** [Release History](CHANGELOG.md)\n- 📧 **Email support:** support@promptoptimizer.xyz\n\n---\n\n*Start free at [promptoptimizer.xyz](https://promptoptimizer.xyz) — 20 LLM optimizations/month, no credit card required.*\n","readmeFilename":"README.md"}