"Understanding Prompts or Prompt Engineering" matching MCP servers:

A minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.

Enables searching for high-quality AI art prompts from Banana Prompts by tags or keyword queries, with direct links to full prompts and images.

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 agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

A Model Context Protocol server that helps users create, validate, manage, and optimize prompts using the RISEN framework (Role, Instructions, Steps, Expectations, Narrowing).

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

A Model Context Protocol server that helps users create, validate, and optimize AI prompts using the RISEN framework (Role, Instructions, Steps, Expectations, Narrowing).

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

MCP server that enables AI coding assistants to audit repositories against engineering rules, generating self-contained HTML reports with citations and optional GitHub issue filing.

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

Enables AI agents to generate, view, and analyze engineering diagrams (RF, PCB, EMC) in draw.io using natural language prompts, with auto-layout and 269 engineering stencils.

Lets AI agents gate actions before they happen with allow/ask/deny verdicts, rank up to 255 options, and report decision stats.

Enables AI assistants to register prompts for security verification, checking for hidden injections, data exfiltration patterns, and jailbreak attempts, then generate embed codes for displaying security badges on websites.

Enables local video inspection and offline transcription with timestamped JSON and Markdown output, enforced filesystem boundaries, and reproducible content-addressed caching.

Enforces team engineering standards across Git, code review, Rails, frontend, deployment, incidents, observability, API design, database, ADRs, and technical debt, with tools for branch name and commit message validation.

A Model Context Protocol server for managing prompt templates as markdown files with YAML frontmatter, allowing users and LLMs to easily add, retrieve, and manage prompts.

Enables local engineering workflow management by consolidating tickets, QA evidence, time tracking, root cause investigation, knowledge, and reporting into a single SQLite database, allowing generation of complete ticket packages for handoffs, dailies, or career evidence.

27 deterministic engineering compliance and calculation tools for the built environment (data-centre PUE and EED, EPBD, NIS2, Eurocode, UAE compliance), callable by AI agents via MCP and REST API. Free tier; every result cites the governing standard.