Free AI prompt API with 185 expert role contexts and 1,741 prompt templates across 24 professional domains. No auth required. Works with any AI agent.
PromptRefiner is a premium micro-service from the M2MCent factory, configured as a Model Context Protocol (MCP) server. Functionality: Optimizador de Prompts Visuales (MJ, Sora). This node features autonomous, secure cryptographic settlement secured by an x402 micro-payment wall on the Base L2 network. Service fee: $0.02 USDC per tool execution.
Turn any AI assistant into an expert prompt engineer. Ultra Prompt gives your AI a library of 1,000+ professionally written prompts for real, everyday work, writing, marketing, business, creative, coding, and more. Just describe what you're trying to do, and it finds the right template, fills in the details, and hands back a polished, ready-to-run prompt, expert-quality results without wasting time writing the best prompt yourself. It can also chain templates into multi-step workflows and save them to reuse. Tools: searchtemplates · gettemplate · buildprompt · suggestvocab · recommendnext · listenhancements · buildpipeline (Pro) · saveplaybook (Pro) Free key: ultraprompt.co → Account → MCP Server. Free: 5 calls/mo · Pro: unlimited.
PromptDNA A community marketplace for composable AI prompt blocks — think npm for prompt engineering. PromptDNA lets developers and AI agents discover, compose, fork, and contribute reusable prompt "blocks" — personas, task templates, constraints, reasoning chains, and output-format specifications that combine to build complete, battle-tested prompts. Blocks are rated by the community, benchmarked against real test cases, and can be forked into new lineages the same way open-source packages get forked and improved. Website: https://promptdna.org MCP endpoint: https://mcp.promptdna.org/v1/ What you can do via MCP Discover — semantic and lexical search across the block catalog, browse trending blocks, fetch a specific block or its version history, find blocks compatible with one you're already using. Compose — assemble a complete prompt from multiple blocks in one call. Contribute — submit new blocks, fork existing ones into your own variant, r
Run a prompt through a LangChain (system + human) chain over Gemini on Vertex AI; optional LangSmith tracing. Ideal for traced/observable LangChain pipelines, evaluating prompts, monitoring chain quality.
Transform basic instructions into high-quality prompts with optimized structure and clear constraints. Evaluate existing prompts for clarity and specificity while receiving actionable improvement suggestions. Access a library of production-ready templates and convert formats to streamline development workflows.
'Oi build this feature for me'. Oi exposes your collaborative team's private agent contexts, guardrails, data connections and workflows, so the same reusable context can be used across ChatGPT, Codex, Claude, Cursor, and other MCP-compatible clients.
Generate PDFs from HTML templates via AI agents or API. List templates, submit jobs with JSON data, check credits, and generate watermarked previews — all through MCP tools. Templates support LiquidJS templating with conditionals, loops, and raw variable injection.
Reduce LLM API costs via semantic caching, prompt compression, model routing and context pruning. Zero code changes required.
Lint and generate AI agent context files (CLAUDE.md, AGENTS.md, Cursor rules, Copilot instructions) from any MCP client. The linter is free, needs no account, and applies ~30 research-backed rules; generation uses a PromptArch API key.
One workspace for your prompts, documents, and collections — accessible from every AI client you use. Context Repo is an AI context management platform for capturing, organizing, versioning, and searching the knowledge artifacts you use with AI tools. This MCP server exposes 28 tools that give Claude, Cursor, ChatGPT, Factory, Windsurf, Codex, Claude Code, and any other MCP-compatible client direct read-and-write access to your personal workspace — no copy-paste, no context loss between conversations. Features Prompts — Full CRUD with version history, rollback, and semantic search across your prompt library Documents — Markdown and plain-text storage with automatic chunking and 1536-dim vector embeddings Collections — Named folders that group prompts and documents into project-scoped contexts Catalog Search — find_items returns ranked results across prompts, documents, and collections in a single call (semantic by default, literal fallback) **Dee
Full AI agent lifecycle platform — create, grade, optimize, deploy and orchestrate AI agents from your IDE or terminal. What you can do Build — Create agents from 37 templates or custom system prompts, with skills, tools and extensions Grade — Evaluate agent quality with 6 criteria (output match, schema validation, tool usage, safety, custom scripts, LLM judge) Optimize — AutoResearch lab with 6 modes: AutoTune, AutoFix, Stress Lab, Tournament, Distillation, Evolution Deploy — Widget, Slack, Telegram, WhatsApp, webhooks (n8n/Zapier/Make compatible) Orchestrate — Multi-agent teams with parallel, sequential and conditional execution modes Monitor — Usage tracking, session history, compliance reports (EU AI Act) 32 tools in 6 categories | Category | Tools | |----------|-------| | Agent CRUD | create, get, update, delete, list, import, export | | Templates | list templates, deploy template | | Grading | grade prompt, create suite, run grading,
Production-ready prompt injection detection for AI agents. Scan user input, retrieved docs, and tool outputs before passing them to an LLM. Returns injectiondetected, score, attacktype, and sanitized text.
PQS is the fastest way to get better output from any AI model. Score any prompt before it hits the model. Get a grade (A-F), score out of 80, percentile, and dimension breakdown across 8 quality dimensions. Built on PEEM, RAGAS, MT-Bench, G-Eval, and ROUGE frameworks. Pre-flight, not post-hoc. The AI input quality problem is real. PQS solves it. MCP Tools: scoreprompt: Free. Returns grade (A-F), score out of 80, percentile, and full 8-dimension breakdown. No API key needed. optimizeprompt: $0.025 USDC. Returns a rewritten, higher-scoring version of your prompt with before/after dimension deltas. HTTP API (x402-native on Base): /api/score/free: Free. Grade, percentile, and 8-dimension breakdown. No payment required. /api/score: $0.025 USDC. Single score, pay-per-call. /api/score/full: $0.125 USDC. Grade, percentile, dimension breakdown, and rewrite. /api/score/batch: $0.25 USDC. Score multiple prompts in a single call. /api/preflight: $0.05 USDC. Lightweight pre-flig
Generate optimized prompts for over 140 platforms using Prompeteer's Contextual AI platform. Manage and organize your creations within a personal vault for easy retrieval and storage. Evaluate prompt quality across multiple dimensions to ensure high-performance results.
Prompt improver for people who never know what to type into AI. Rewrites a rough request (Thai or English) into a sharp prompt structured as Role, Task, Context, Format. Hosted server, free tier included: 5 credits/day, no sign-up. Tools: improveprompt, buildprompt. Website: https://rtcf.happlen.com
ModelBound is a platform to author, test, and govern AI context specs (system prompts, rules, skills, MCP) across teams. AI context files (system prompts, rules, skills, MCP configs) are scattered across repos, IDEs, and platforms with no version control, testing, or governance — so teams ship brittle agents and waste tokens on bloated, untested context. ModelBound gives teams one place to author, test, and govern all of it across Claude, Cursor, Copilot, Bedrock, and beyond.
Generate personas for testing and prototyping. Build from your connected accounts or custom guidance to mirror real-world behavior. Export in multiple formats for easy sharing and integration.
Token-efficient web browser for LLM agents
Persistent memory and context layer for AI agents. inject() before your LLM call, update() after. Relevance-scored injection, grant-based access control, user-owned context. Works with OpenAI, Anthropic, Vercel AI SDK, and LangChain. < 50ms retrieval. GDPR-ready. Free tier: 2,500 calls/month.
An MCP (Model Context Protocol) server that turns your ReportFlow templates into PDF reports — invoices, contracts, statements, anything you've designed — straight from Claude or any other MCP-compatible AI agent. What it does Generate PDFs from natural-language requests like "create an invoice for Acme Corp totalling $300" Expose your ReportFlow designs and their parameter schemas directly to the AI as MCP Resources Bulk-generate many PDFs and download them as a single ZIP Save outputs to whichever workspace folder the user is currently in (Claude Desktop / Claude Code / Cursor / VS Code all supported) Setup Claude Desktop / Claude Code / Cursor Add the following to your config file (.mcp.json, claudedesktopconfig.json, ~/.cursor/mcp.json, etc.): { "mcpServers": { "reportflow": { "command": "npx", "args": ["-y", "reportflow-mcp"] } } } That's the whole setup. No env vars, no API keys, no secrets to manage. VS Code (MCP-enabled builds) Same JSON in .vs
