"An introduction to prompt engineering" matching MCP servers:

An MCP server that exposes three tools for auditing, scaffolding, and triaging agent intent specs against a unified template, helping prevent intent failures in AI agents.

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

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.

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

Enables repository-aware lifecycle management and controlled delegation of coding tasks to trusted worker harnesses via MCP, with execution isolation, recovery, and verified handoff.

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.

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.

Automatically analyzes and optimizes AI prompts by calculating clarity scores, detecting risks, asking clarifying questions, and adding domain-specific requirements to improve AI interaction quality.

An MCP server that automatically optimizes AI prompts using evolutionary algorithms, helping improve prompt performance, creativity, and reliability through iterative testing and refinement.

Enables cleaning and sanitizing prompts through an LLM-powered tool that removes sensitive information, provides structured feedback with notes and risks, and normalizes prompt formatting. Supports configurable local or remote OpenAI-compatible APIs with automatic secret redaction.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

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.

Enables AI coding agents to request user input through customizable desktop forms, returning structured data.

A deterministic prompt optimization middleware for AI coding agents that preserves intent, compresses context, and detects drift, available as an MCP server with tools for optimize_prompt, classify_prompt, and more.

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

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

A Model Context Protocol server that converts Markdown content to HTML format.

A server that transforms a standard Language Model into a dynamic multi-agent system where the model simulates both a Conductor (project manager) and Experts (specialized agents) to tackle complex problems through a collaborative workflow.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.