"Créer des systèmes de prompt pour les modèles de langage (LLM)" matching MCP servers:

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.

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.

Blind multi-model councils with anonymous LLM seats and direct 1:1 chat as a local MCP server. Enables asking one question to get independent answers from multiple model CLIs, then blind scoring and reveal.

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.

MCP server that provides a live coordination layer for AI agents, including attributable handoffs, a shared event ledger, atomic work-claiming, and advisory file leases to prevent collisions.

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 provides LLMs access to other LLMs

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.

Provides strategic dialogue management and emotional de-escalation for customer support bots, detecting problem patterns and recommending actions like escalation to a human operator.

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.

MCP server that provides tools for evaluating LLM agent reliability, including adversarial task generation, automated LLM-as-judge assessment, and confidence statistics.

An MCP server that lets Claude Code consult stronger AI models (o3, Gemini 2.5 Pro, DeepSeek Reasoner) when you need deeper analysis on complex problems.

Centralized encrypted gateway that routes requests to 11+ LLM providers (API keys and CLI subscriptions) through a single OpenAI-compatible endpoint, with MCP tools for vault operations, code search, and shared state.

Enables AI coding agents to run multi-model council deliberations by concurrently querying OpenAI and DeepSeek for architectural planning and adversarial critique, then synthesizing the results into actionable implementation decisions.

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

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 Claude Code as a team leader to delegate tasks to GPT and Gemini experts, supporting multi-LLM collaboration with tools for consultation, code review, design, and background execution.