What can you do with this server?

The server provides tools for managing and querying project context in LLM-assisted development with three primary functions: * **Generate Repository Overview**: Creates a structured overview with directory trees, file statuses, key file contents, and smart outlines for supported languages * **Retrieve File Contents**: Fetches complete contents of specific files for analysis or text searches * **List Modified Files**: Tracks files changed since a given timestamp, helping identify updates during conversations The server also supports customization through profiles to define file inclusion rules and presentation formats for tailored outputs, while managing context to avoid redundant requests.

LLM Context

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Reduce friction when providing context to LLMs. Share relevant project files instantly through smart selection and rule-based filtering.

The Problem

Getting project context into LLM chats is tedious:

Manually copying/pasting files takes forever
Hard to identify which files are relevant
Including too much hits context limits, too little misses important details
AI requests for additional files require manual fetching
Repeating this process for every conversation

Related MCP server: Bifrost VSCode Devtools

The Solution

lc-select # Smart file selection lc-context # Instant formatted context # Paste and work - AI can access additional files seamlessly

Result: From "I need to share my project" to productive AI collaboration in seconds.

Note: This project was developed in collaboration with several Claude Sonnets (3.5, 3.6, 3.7 and 4.0), as well as Groks (3 and 4), using LLM Context itself to share code during development. All code in the repository is heavily human-curated (by me 😇, @restlessronin).

Installation

uv tool install "llm-context>=0.5.0"

Quick Start

Basic Usage

# One-time setup cd your-project lc-init # Daily usage lc-select lc-context

MCP Integration (Recommended)

{ "mcpServers": { "llm-context": { "command": "uvx", "args": ["--from", "llm-context", "lc-mcp"] } } }

With MCP, AI can access additional files directly during conversations.

Project Customization

# Create project-specific filters cat > .llm-context/rules/flt-repo-base.md << 'EOF' --- compose: filters: [lc/flt-base] gitignores: full-files: ["*.md", "/tests", "/node_modules"] --- EOF # Customize main development rule cat > .llm-context/rules/prm-code.md << 'EOF' --- instructions: [lc/ins-developer, lc/sty-python] compose: filters: [flt-repo-base] excerpters: [lc/exc-base] --- Additional project-specific guidelines and context. EOF

Deployment Patterns

Choose based on your LLM environment:

System Message: lc-context -p (AI Studio, etc.)
Single User Message: lc-context -p -m (Grok, etc.)
Separate Messages: lc-prompt + lc-context -m
Project/Files (included): lc-context (Claude Projects, etc.)
Project/Files (searchable): lc-context -m (force into context)

See Deployment Patterns in the user guide for details.

Core Commands

Command	Purpose
`lc-init`	Initialize project configuration
`lc-select`	Select files based on current rule
`lc-context`	Generate and copy context
`lc-context -p`	Generate context with prompt
`lc-context -m`	Send context as separate message
`lc-context -nt`	No tools (for Project/Files inclusion)
`lc-context -f`	Write context to file
`lc-set-rule <n>`	Switch between rules
`lc-missing`	Handle file and context requests (non-MCP)

Rule System

Rules use a systematic five-category structure:

Prompt Rules (: Generate project contexts (e.g., lc/prm-developer, lc/prm-rule-create)
Filter Rules (: Control file inclusion (e.g., lc/flt-base, lc/flt-no-files)
Instruction Rules (: Provide guidelines (e.g., lc/ins-developer, lc/ins-rule-framework)
Style Rules (: Enforce coding standards (e.g., lc/sty-python, lc/sty-code)
Excerpt Rules (: Configure extractions for context reduction (e.g., lc/exc-base)

Example Rule

--- description: "Debug API authentication issues" compose: filters: [lc/flt-no-files] excerpters: [lc/exc-base] also-include: full-files: ["/src/auth/**", "/tests/auth/**"] --- Focus on authentication system and related tests.

AI-Assisted Rule Creation

Let AI create focused rules for specific tasks. There are two approaches depending on your setup:

Approach 1: Claude Skill (Recommended for Claude Desktop/Code)

How it works: A global Claude Skill helps you create rules interactively. It requires project context (with overview) already shared via llm-context, and uses lc-missing to examine specific files as needed.

Setup:

lc-init # Installs skill to ~/.claude/skills/ # Restart Claude Desktop or Claude Code

Workflow:

# 1. Share any project context (overview is required) lc-context # Can use any rule - overview will be included # 2. Paste into Claude # 3. Ask the Skill to help create a rule # "Create a rule for refactoring authentication to JWT" # "I need a rule to debug the payment processing system"

Claude will:

Use the project overview already in context
Use lc-missing to examine specific files as needed for deeper analysis
Ask clarifying questions about scope and focus
Intelligently select relevant files (5-15 full, 10-30 excerpted)
Generate optimized rule configuration
Save to .llm-context/rules/tmp-prm-<task-name>.md
Provide instructions for testing the rule

Skill Files:

Skill.md - Quick workflow and patterns (always loaded)
PATTERNS.md - Common rule patterns (on demand)
SYNTAX.md - Detailed syntax reference (on demand)
EXAMPLES.md - Complete walkthroughs (on demand)
TROUBLESHOOTING.md - Problem solving (on demand)

Skill Updates: Automatically updated when you upgrade llm-context. Restart Claude to use the new version.

Approach 2: Prompt-Based with Instruction Rules (Works Anywhere)

How it works: You use a project rule that loads comprehensive rule-creation documentation as context.

Setup: No special setup needed - the documentation is built-in.

Usage:

# 1. Load the rule creation framework into context lc-set-rule lc/prm-rule-create lc-select lc-context -nt # 2. Paste into any LLM and describe your task # "I need to add OAuth integration to the auth system" # 3. The LLM generates a focused rule using the framework # 4. Use the focused context lc-set-rule tmp-prm-oauth-task lc-select lc-context

Documentation Included:

lc/ins-rule-intro - Chat-based rule creation introduction
lc/ins-rule-framework - Comprehensive decision framework, semantics, and best practices

Comparison

Aspect	Skill	Instruction Rules
Setup	Automatic with `lc-init`	Already available
Requires project context	Yes (overview needed)	Yes (overview needed)
Interaction	Interactive, multi-turn in Claude	Static documentation in context
Exploration	Uses `lc-missing` as needed	Manual or via AI requests
Best for	Claude Desktop/Code users	Any LLM, API, automation

Both approaches require sharing project context first via lc-context. They produce equivalent results - choose based on your environment and preference.

Workflow Patterns

Daily Development

lc-set-rule lc/prm-developer lc-select lc-context # AI can review changes, access additional files as needed

Focused Tasks

# Share project context lc-context # Then ask Skill (Claude Desktop/Code): # "Create a rule for [your task]" # Or work with any LLM using instruction rules: # lc-set-rule lc/prm-rule-create && lc-context -nt

MCP Benefits

Code review: AI examines your changes for completeness/correctness
Additional files: AI accesses initially excluded files when needed
Change tracking: See what's been modified during conversations
Zero friction: No manual file operations during development discussions

Key Features

Smart File Selection: Rules automatically include/exclude appropriate files
Instant Context Generation: Formatted context copied to clipboard in seconds
MCP Integration: AI can access additional files without manual intervention
Systematic Rule Organization: Five-category system for clear rule composition
AI-Assisted Rule Creation: Two approaches - interactive Skill or documentation-based
Code Excerpting: Extractions of significant content to reduce context while preserving structure

Learn More

License

Apache License, Version 2.0. See LICENSE for details.

llm-context