LLM Context
Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation). Includes smart code outlining.
What is LLM Context?
LLM Context is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to share code with llms via model context protocol or clipboard. rule-based customization enables easy switching between different tasks (like code review and documentation). includes smart code outlinin...
Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation). Includes smart code outlining.
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Share code with LLMs via Model Context Protocol or clipboard
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx llm-context-pyConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use LLM Context
LLM Context (llm-context.py) is a Python tool that packages focused, rule-filtered slices of your codebase and delivers them to AI coding assistants either via the clipboard or through an MCP server connection. It uses composable rule profiles stored in .llm-context/ to define exactly which files to include for a given task — so the context for a code review differs from the context for writing documentation. The MCP tools lc_outlines, lc_preview, and lc_missing let Claude request project structure and individual files on demand, dramatically reducing token waste compared to blindly pasting entire directories.
Prerequisites
- Python 3.10+ and the uv package manager (https://github.com/astral-sh/uv)
- Claude Desktop, Claude Code CLI, or another MCP-compatible client
- A local code repository — run lc-init from the project root before first use
- No external API keys required
Install llm-context with uv
Install the package as a uv tool so the lc-mcp, lc-init, lc-select, and lc-context commands are globally available.
uv tool install "llm-context>=0.6.0"Initialize your project
Navigate to your repository root and run lc-init. This scaffolds the .llm-context/ configuration directory and copies skill files to .claude/skills/.
cd /path/to/your/repo
lc-initDefine or review rule profiles
Edit the rule files inside .llm-context/ to control which files each named profile includes. Run lc-preview with a profile name to check its coverage before using it.
lc-preview code-reviewAdd the MCP server to Claude Desktop
Register the lc-mcp command in your claude_desktop_config.json. Claude will then have access to the lc_outlines, lc_preview, and lc_missing tools for your projects.
Restart Claude Desktop and test the connection
Restart Claude Desktop after editing the config. Open a conversation, navigate to your project folder, and ask Claude to call lc_outlines to verify the server responds correctly.
LLM Context Examples
Client configuration
Add this to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) to register the llm-context MCP server.
{
"mcpServers": {
"llm-context": {
"command": "uvx",
"args": ["--from", "llm-context", "lc-mcp"]
}
}
}Prompts to try
Use these prompts after the MCP server is connected to leverage project context tools.
- "Call lc_outlines to give me a structural overview of this project"
- "Use lc_preview with the 'documentation' rule to show me what files that profile covers"
- "Fetch the contents of src/auth/session.py using lc_missing"
- "Switch to the code-review rule profile and then review the recent changes to the API layer"Troubleshooting LLM Context
lc-init reports permission errors or fails to create .llm-context/
Ensure you have write permissions to the project root directory and that you are running lc-init from within the repository, not from a parent directory.
lc_outlines returns an empty result in Claude
Verify the .llm-context/ directory exists in your project root and that the active rule profile's file globs match at least some files. Run 'lc-preview <rule>' in the terminal to debug the selection.
Claude Desktop cannot start the lc-mcp server
Confirm uvx is on the PATH visible to Claude Desktop's process. On macOS, add 'export PATH=$HOME/.local/bin:$PATH' to ~/.zprofile and restart the system or re-launch Claude Desktop from a shell that has the updated PATH.
Frequently Asked Questions about LLM Context
What is LLM Context?
LLM Context is a Model Context Protocol (MCP) server that share code with llms via model context protocol or clipboard. rule-based customization enables easy switching between different tasks (like code review and documentation). includes smart code outlining. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install LLM Context?
Follow the installation instructions on the LLM Context GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with LLM Context?
LLM Context works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is LLM Context free to use?
Yes, LLM Context is open source and available under the Apache-2.0 license. You can use it freely in both personal and commercial projects.
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Set Up LLM Context in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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