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

MCP server for Taiga project management, enabling LLM clients to inspect and manage projects, work items, sprints, comments, attachments, and wiki pages via 6 op-dispatching tools.

Enables AI assistants to work with Linear via the Model Context Protocol, covering issue/project management, resource and prompt access, webhooks, and OAuth app setup.

A demo MCP server exposing an in-memory todo list via tools (add_task, list_tasks, complete_task), a resource, and a prompt, over both stdio and HTTP transports.

Enables LLM clients to answer prospect questions about Timers Studio, a professional broadcast timer platform, with first-party data on competitive positioning, use cases, personas, and recommended plans.

Enables traceable requirement discovery, technical alignment, and ISO-aligned process checking through deterministic MCP tools and resources, without requiring an embedded LLM.

Enables users to clarify vague goals through Socratic questioning and generate structured technical specifications, using a curated knowledge base and session state without external LLM APIs.

Provides a local, agent-maintained product context layer over MCP, serving structured strategy, competitor, customer feedback, and feature inventory data to any AI tool such as Claude, Cursor, or ChatGPT, with local data storage, bring-your-own-LLM support, and weekly evidence-based roadmap reviews that can sync suggestions to Jira or Linear.

Enforces work-readiness gates by tracking project plots, plans, stakeholder views, slots, stops, and journal entries via REST and MCP, with LLM-based plan agreement and result verification.