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

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

MCP server for professional prompt engineering and agentic scaffolding, with cloud and local optimization.

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 a primary agent to offload bounded, high-volume analysis of explicitly selected workspace files to an auxiliary LLM through a single read-only tool, returning text, Markdown, JSON, or unified-diff output with token usage while retaining all planning, verification, and edits.

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.

A small MCP server that lets Claude (or any MCP client) ask other models what they think.

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

Wraps Tool and ToolPackage instances from the llm-chat ecosystem behind the Model Context Protocol, enabling stdio and Streamable HTTP transports for MCP servers.

Enables cost-aware LLM routing over MCP with verification-based cascading, offering tools for routing queries, resuming escalated approvals, estimating costs, comparing policies, and explaining routing decisions, plus read-only resources and a policy-selection prompt.

A local-first LLM routing MCP server that keeps sensitive data on your own models, with fail-closed privacy and manager-worker delegation, exposing route and complete tools to any MCP client.

Enables delegating mechanical or high-volume subtasks to a local LLM through an MCP tool, letting the assistant query the local model without using its own output tokens for content.

A thin MCP server that delegates lightweight tasks from Claude Code or any MCP-compatible client to local or cloud LLMs via LiteLLM, supporting models like Ollama and cloud APIs as subagents.

A local MCP gateway that unifies multiple LLM providers, API keys, routing, and multi-model discussions into a single tool surface.

Enables a cloud orchestrator to query a local Ollama model over Tailscale or a LAN as a side-channel for drafts, summaries, and other helper output, with tools to list available models and to redact text. Answers leaving the server are passed through output-only DLP that masks card numbers, IBANs, emails, phone numbers, API keys, and similar secrets before they reach the cloud.

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.

A Model Context Protocol server that provides unified access to multiple LLM APIs including ChatGPT, Claude, and DeepSeek, allowing users to call different LLMs from MCP-compatible clients and combine their responses.

Routes your AI tasks to the best available model across 20+ providers — automatically selecting based on task type, budget, and subscription pressure. Supports text, image, video, and audio with built-in cost optimization and fallback chains.

A Model Context Protocol (MCP) gateway for running Claude Code, Codex, Gemini, Grok, and Mistral (Vibe) CLIs from one MCP endpoint, with durable async jobs, session continuity, cache-aware prompting, observability, and personal-appliance setup tooling. Why developers try it: one local MCP endpoint for cross-LLM validation, multi-agent coding workflows.

Route prompts intelligently across Claude, Gemini, and GPT-4o, automatically picking the best model for every task while minimizing token cost.

Enables AI agents to interact with multiple LLM providers (OpenAI, Anthropic, Google, DeepSeek) through a standardized interface, making it easy to switch between models or use multiple models in the same application.