LLM Context MCP Server

Overview

LLM Context is a lightweight MCP server designed to streamline the way developers share project artifacts with large language models. In typical AI‑driven development workflows, a programmer must manually curate which files to copy into a chat, ensuring the model receives enough information without exceeding token limits. LLM Context automates this tedious step by providing a rule‑based file selector and context generator that can be invoked directly from the command line or integrated into an MCP‑enabled assistant. The result is a seamless transition from “I need to share my project” to productive AI collaboration in seconds.

The core value proposition lies in its intelligent context packaging. By defining rules—comprising prompts, filters, instructions, styles, and excerpts—developers can tailor the exact slice of code, documentation, or configuration that is relevant to a given task. Filters remove noise such as test harnesses or third‑party dependencies, while excerpt rules trim large files to their most pertinent sections. Prompt and instruction rules embed developer‑specific guidelines or coding standards, allowing the model to understand not only what is in the repository but also how it should be interpreted. This precision prevents the “context overflow” problem that plagues many LLM chats, ensuring that token budgets are respected while still delivering actionable information.

In practice, LLM Context shines in scenarios where rapid iteration and deep code understanding are required. A backend engineer can ask the model to review a new authentication flow, and the server automatically supplies only the relevant source files, tests, and configuration snippets. A frontend team can request a refactor of a UI component; the MCP server provides the current implementation and any associated style guides, enabling the assistant to generate refactored code that adheres to project conventions. Because the server exposes a standard MCP interface, any Claude or Grok‑based assistant can request additional files on demand, eliminating the need for manual file transfer during a conversation.

Integration is straightforward: developers install the package and add an MCP server entry pointing to the bundled command. Once registered, the assistant can invoke commands such as or , receiving a ready‑to‑paste context payload. The server also supports non‑MCP environments via the flag, making it versatile across tooling ecosystems. By embedding context selection into the assistant’s dialogue loop, developers experience a frictionless workflow where the AI can “see” the codebase as naturally as a human teammate.

Unique advantages include its declarative rule system, which encourages reusable and shareable configurations across teams, and its focus on token‑efficiency through excerpting. The project’s development history—built with multiple Claude and Grok models using LLM Context itself—underscores its robustness and community trust. For developers looking to reduce context‑management overhead, LLM Context offers a principled, extensible solution that fits cleanly into existing AI‑augmented development pipelines.