Llm Context.Py MCP Server

Llm context.py mcp server lets Claude, Cursor and other MCP clients work with Llm Context.Py directly. Getting the right context into LLM conversations is friction-heavy:.

What Llm Context.Py does

Getting the right context into LLM conversations is friction-heavy:

Key capabilities

• Intelligent Selection — Rules automatically include/exclude appropriate files
• Context Validation — Preview size and selection before generation
• Code Excerpting — Extract structure while reducing tokens (15+ languages)
• MCP Integration — AI accesses additional files without manual intervention
• Composable Rules — Build complex contexts from reusable patterns
• AI-Assisted Creation — Interactive skill or documentation-based approaches
• Agent-Friendly — CLI and MCP interfaces for autonomous operation

Tools it exposes

Once connected, the assistant can call these 6 tools directly:

• lc-init — Initialize project configuration
• lc-select — Select files based on current rule
• lc-context — Generate and copy context
• lc-outlines — Get code structure excerpts
• lc-missing — Fetch files/implementations (manual MCP)
• Comparison — Both require sharing project context first. Both produce equivalent results

Installing the llm context.py mcp server

Setup follows the standard MCP pattern: clone or install the server, then register it in your client's configuration file and restart the client. The configuration snippets on this page cover Claude Desktop, Claude Code and Cursor.

Where it fits

AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. Llm Context.Py sits in that group, and the shape of its toolset — lc-init, lc-select, lc-context among others — tells you what it is really for. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.

Practical notes

• This server runs locally, so it operates with whatever access your machine and its credentials already have. Scope that deliberately rather than by default.
• Maintained by cyberchitta, written in Python.
• MCP clients ask for confirmation before each tool call by default. Keep that on while you learn what the llm context.py mcp server actually does with your data.
• Every entry in this directory is reviewed by hand before it goes live, and details are checked against the project's own documentation.