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

Model Context Protocol (MCP) server for German Electronic Invoicing (ZUGFeRD 2.x / XRechnung 3.x). Provides tools to validate, generate, parse, and convert invoices compliant with EN 16931 and KoSIT.

MEOK NIS2 De Register - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables querying the Pour Picks bourbon & whiskey database with tools for search, bottle details, recommendations, comparisons, and trending, providing structured tasting profiles, prices, pairings, and ratings.

MCP server exposing prompt-shield's prompt-injection firewall for LLM applications. Four tools — scan_input, scan_output, scan_tool_result (with optional sanitization), and list_detectors — backed by 34+ detectors and a federated ed25519-signed threat feed.

WAF for AI agents — block prompt injection before it reaches the LLM.

LLM Compliance Comparison - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables coding agents to watch and analyze videos by extracting scene-aware frames, transcribing speech, and generating shot timelines, all constrained by a token budget to fit LLM context limits.

Enables AI assistants across platforms to share memory, recall facts, preferences, and conversation history, creating a unified cognitive system.

Optimizes token costs by intelligently delegating low-complexity tasks to local LLMs via LiteLLM, enabling cost-effective development workflows.

Enables clients to optimize prompt prefix caching by matching longest common token prefixes with a radix trie, eliminating redundant prefill computation and reducing time-to-first-token.

Enables deterministic detection and neutralization of adversarial prompt injections and override attempts in AI agent workflows via a zero-dependency MCP server, providing structured telemetry and low-latency validation.

Enables autonomous agents and MCP-compatible clients to route inputs through a deterministic firewall that flags and sanitizes prompt injection, jailbreaks, system prompt exfiltration, and tool hijacking. It supports JSON-RPC stdio integration for tools like Claude Desktop, Cursor, and multi-agent frameworks.

Enables running deterministic multi-model prompt regression and golden dataset benchmark suites, exposing structured results and telemetry through the Model Context Protocol for integration with MCP-compliant clients.

Enables MCP clients and autonomous agents to screen incoming prompts for system overrides, delimiter breakouts, and encoded payload evasion, helping block prompt injection and jailbreak attempts before execution.

Provides tools to retrieve the GodPrompt universal system prompt and its components (core skill, protocols, gates, anti-patterns) for AI software development, plus task classification. Designed for progressive context usage.

Enables MCP-compliant clients to sanitize prompt payloads by deterministically masking and reversibly redacting PII before they reach LLMs, returning structured output and telemetry.

Live LLM API pricing: current token prices, model comparisons, cheapest-model lookups, and The LLM Price Index for 150+ models across 20+ providers, re-verified daily. No API key required.

Discover audio and LLM services, compare seven voices, and check prices for free. Generate speech, transcribe audio and request LLM text with optional capped USDC payments on Base; voice-cloning requirements are also available.

Analyzes, refines, and optimizes prompts for AI assistants by fixing grammar, improving clarity, applying best practices like chain-of-thought and few-shot learning, and scoring prompt quality across multiple dimensions.

MindBase implements Karpathy's "LLM wiki" pattern as an MCP server. Instead of retrieving chunks at query time (RAG), the LLM maintains a persistent wiki between your conversations: it reads every source you feed it, cross-references pages with [[wikilinks]], flags contradictions between sources, and keeps a context.md that reflects your current synthesized thinking.