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

Unofficial MCP server for the messdienst24.de utility portal, providing tools to retrieve heating and hot-water consumption data, download PDF reports, and compare usage trends.

Provides coding agents with visibility into test health through tools for flaky test detection, test quality linting, and LLM evaluation harness, enabling them to triage failures, review test quality, and check prompt changes for regressions.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

Enables any MCP-compatible LLM to perform AI-powered web research using Google's AI Mode, returning synthesized and cited answers instead of raw search results.

An MCP server that lets LLM agents operate the IDnow Trust Platform sandbox to list verification flows, create sessions, check sessions, and get session details.

An enhanced MCP server that enables users to interact with NotebookLM while ensuring strict source fidelity and citation accuracy through client-side prompt structuring. It guides Claude to transform user queries into structured prompts that prevent the addition of external knowledge during document analysis.

DevEyes is a Model Context Protocol (MCP) server that captures screenshots from your local development environment and automatically optimizes them for LLM consumption.

Enables encoding JSON into compact TOON format and decoding TOON back to JSON, reducing token usage for LLM prompts.

Enables LLM clients to generate standardized test cases, perform quality control with lint scoring, convert to Xray/Jira format, and compose test suites (Smoke/Regression/E2E) with coverage analysis.

A CLI-based Model Context Protocol server that exposes PubNub SDK documentation and Functions resources to LLM-powered tools like Cursor IDE, enabling users to fetch documentation and interact with PubNub channels via natural language prompts.

Enables AI assistants to search, list, and retrieve documentation from the Slice.js official GitHub repository. It provides full-text search capabilities and can deliver individual doc pages or a complete documentation bundle for comprehensive LLM context.

Enables evaluation of AI-generated code across 45 specialized dimensions using deterministic pattern matching and optional LLM-powered deep review, acting as an independent quality gate.

Query Langfuse traces, schema and datasets, scores and metrics, debug exceptions, analyze sessions, and manage prompts. Full observability toolkit for LLM applications.

An MCP server that connects AI assistants to a Neo4j graph database, enabling querying of movie data through tools like graph statistics, genre-based search, and movie details, along with advanced features such as LLM sampling and completions.

Habitica MCP server built with Effect v4, currently exposing a hello-world tool, resource, and prompt over stdio for early development and testing.