"Search related to CMD (Command Prompt)" matching MCP servers:

Routes one brief to the right image model across 60+ (gpt-image-1.5, Ideogram 3, Recraft V4, Flux), validates the output, and fans out to iOS/Android/PWA/favicon/visionOS/Flutter bundles. Works without an API key via Pollinations, HF Inference, Stable Horde, or host-LLM inline SVG.

Enables an MCP-compatible AI client to author Blender Python, run Blender, import the resulting asset into Unity with materials and a prefab, place it in the active scene, verify the import, and revise the same asset using stable IDs.

Enables Command Code to run Kujo tools and Agent Skills locally as MCP tools, including repository inspection, change analysis, and release readiness checks.

MCP server for managing Markdown-Driven Task Management (MDTM) files with task, parent task, environment, and workflow management, enabling structured development workflows.

Provides a local MCP interface to query captured, PII-scrubbed prompt telemetry from coding agents and browser chats, enabling time summaries, prompt search, and activity analysis without data leaving the machine.

Provides a structured map of 120 practical AI prompts across 6 business domains, enabling users to quickly generate complete prompts for specific tasks using slash commands or tools.

Turns a prompt-engineering workflow into a workbench: it suggests candidate prompts with live sample replies, gives them LangGPT structure, rewrites them over multiple rounds, and proves the result against test cases with promptfoo-style assertions, model graders and DSPy-style train/dev search. Runs on any OpenAI-compatible provider using your own API key.

Local MCP server that uses a local Ollama model to rewrite rough prompt drafts into structured, optimized prompts for paid APIs, saving tokens and improving output quality.

Generates and manages slash commands for AI coding assistants like Claude Code and Cursor, supporting both interactive CLI and programmatic MCP access.

An MCP server that uses Claude 3.5 Sonnet to transform ordinary prompts into structured, professionally engineered instructions for any LLM. It enhances AI interactions by adding context, requirements, and structural clarity to raw user inputs.

Enables AI agents to turn rough or ambiguous requests into clearer, context-aware execution guidance through optimization, inspection, adaptation, evaluation, comparison, context compression, and instruction-building tools while preserving original intent and staying local-first.

Optimizes prompts using meta-prompting techniques through a two-turn approach that first provides optimization guidelines and then refines the prompt for enhanced quality and effectiveness.

The control plane for AI prompts that scores, enforces policy, locks config, and audits every prompt decision.

Enables AI models like Claude to manage local prompt files with CRUD operations, fuzzy search, categorization, templates, version control, and favorites.

An MCP server that registers customizable prompt templates as tools for natural language interaction. It supports hot-reloading and easy extension via YAML or JSON files to automate tasks like code review, refactoring, and content summarization.

A Model Context Protocol server that provides specialized prompt suggestions for backend development, frontend development, and general tasks to help LLMs generate better content.

A server based on Model Context Protocol that provides predefined prompt templates for tasks like code review and API documentation generation, enabling more efficient workflows in Cursor/Windsurf editors.

A local-only MCP server that uses SQLite to store, search, and manage a personal library of AI prompts. It enables developers to organize and reuse prompts across multiple AI clients like Claude and Cursor while keeping all data on their local machine.

Smart AI workflow for Cursor teams with intelligent prompt routing, real-time cost optimization, and team analytics.

A local MCP server for prompt evaluation, enabling users to define test cases, run prompts against multiple LLM providers, score outputs with deterministic and LLM-graded assertions, and track quality over time, all within an AI coding environment.