"Understanding Cache for Prompt Engineering" matching MCP servers:

MCP server for multimodal understanding and object grounding (bounding boxes) across images, videos, and documents, with support for multiple AI providers (Zhipu GLM-V, OpenAI GPT-4o, Anthropic Claude, or any OpenAI-compatible endpoint).

Enables AI models to interactively prompt users for input or clarification directly through their code editor. It facilitates real-time communication between assistants and users during development tasks.

Exposes get, set, and delete operations over a Redis/Valkey-compatible key-value store as MCP tools, with per-user key namespacing and TTL support on writes. Enables clients to cache and retrieve arbitrary string values statelessly over SSE or direct HTTP, using their own backend.

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

Exposes a protected, stateless MCP endpoint over a Cloudflare-hosted camera pipeline, letting agents list active devices and pull hour- or minute-level JPEG contact sheets for any time window. Agents can also enumerate exact capture timestamps, compare 2–10 chosen frames in a single derived image, fetch unmodified original frames, and generate temporary download links.

Integrates Xiaomi MiMo's multimodal API to enable understanding of images, audio, and video through natural language prompts.

Analyzes, generates, simulates, and optimizes caching strategies across HTTP, CDN, and browser layers for AI agents.

Lets MCP hosts search the public Image3 GPT Image 2 prompt gallery, fetch complete prompts, list categories, and turn rough ideas into structured prompt briefs.

An MCP server that gives an AI assistant a set of real mechanical-engineering tools. Provides deterministic tools like material properties, beam analysis, unit conversion, and curve fitting for engineering calculations.

Enables deterministic, read-only design of OpenSora video prompts and shot plans, including prompt diagnosis and access to workflow guides, without generating videos or requiring API keys.

Enables prompt optimization loops and regression test suites for Claude Code, with a companion web UI for real-time visualization of scores and prompt revisions.

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 searching and browsing a curated matrix of 100+ engineering tools by name, tag, or use case, and retrieving ready-to-use boilerplate files for 30+ popular stacks.

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.

Manages AI prompts stored in Notion databases, enabling search, retrieval, and management across multiple prompt collections.

A lightweight MCP server that provides a unified interface to various LLM providers including OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, and Ollama.

Enables agents to deduplicate semantic prompt caches by measuring Jaccard token overlap, preventing redundant LLM calls and reducing cost. It provides deterministic lookup-or-insert cache behavior through an MCP-compatible interface.

An MCP server that allows agents to test and compare LLM prompts across OpenAI and Anthropic models, supporting single tests, side-by-side comparisons, and multi-turn conversations.

Enables AI-assisted development by providing a library of 110+ curated prompts, workflows, and coding standards, with MCP tools for prompt retrieval, composition, and multi-step workflow chains.

This MCP server provides research-backed prompt optimization tools and professional domain templates designed to improve AI performance through strategies like Tree of Thoughts and Medprompt. It enables users to analyze, auto-optimize, and refine prompts using advanced reasoning patterns and safety-critical alignment techniques.