largefile 0.1.5

MCP server for AI assistants to navigate, search, and edit large codebases, logs, and data files with semantic code analysis

Largefile MCP Server

Navigate, search, and edit large codebases, logs, and data files that exceed AI context limits.

Why Largefile?

• Go beyond context limits - Read, search, and edit files too large to fit in AI context windows
• Semantic code navigation - Tree-sitter extracts functions/classes for Python, JS/TS, Rust, Go
• Fewer LLM errors - Search/replace editing eliminates line number mistakes common with line-based edits
• Smart search - Fuzzy matching, regex, case-insensitive, inverted, and count-only modes
• No size limits - Handles multi-GB files via tiered memory strategy (RAM → mmap → streaming)

Quick Start

Prerequisite: Install uv for the uvx command.

{
  "mcpServers": {
    "largefile": {
      "command": "uvx",
      "args": ["--from", "largefile", "largefile-mcp"]
    }
  }
}

Tools

Tool	Use For
get_overview	File structure and semantic outline before diving in
search_content	Finding patterns, counting occurrences, regex matching
read_content	Reading specific sections; tail/head modes for logs
edit_content	Safe search/replace with automatic backups
revert_edit	Recovering from bad edits

When to Use Largefile

Use when:

• File exceeds ~1000 lines or 100KB (supports multi-GB files)
• Navigating large codebases with semantic structure
• Analyzing log files (especially recent entries with tail mode)
• Making search/replace edits across large files
• Counting occurrences without loading full content

Don't use for:

• Small files that fit in context (AI doesn't need help with those)
• Binary files (images, executables, compressed)

Usage Examples

Large Codebase Navigation

# Get semantic structure of a large Python file
overview = get_overview("/path/to/large_module.py")
# Returns: 2,847 lines, 15 classes, function outline via Tree-sitter

# Find all class definitions
classes = search_content("/path/to/large_module.py", "class ", fuzzy=False)

# Read complete class with semantic chunking
code = read_content("/path/to/large_module.py", pattern="class UserModel", mode="semantic")

Batch Refactoring

# Preview rename across file
preview = edit_content("/path/to/api.py", changes=[
    {"search": "process_data", "replace": "transform_data"},
    {"search": "old_endpoint", "replace": "new_endpoint"}
], preview=True)

# Apply changes (creates automatic backup)
result = edit_content("/path/to/api.py", changes=[...], preview=False)

# Undo if needed
revert_edit("/path/to/api.py")

Log Analysis

# Get log file overview
overview = get_overview("/var/log/app.log")
# Returns: 150,000 lines, 2.1GB

# Read last 500 lines efficiently
recent = read_content("/var/log/app.log", limit=500, mode="tail")

# Count errors without loading content
error_count = search_content("/var/log/app.log", "ERROR", count_only=True, fuzzy=False)

# Find errors with regex
errors = search_content("/var/log/app.log", r"ERROR.*timeout", regex=True)

Supported Languages

Tree-sitter semantic analysis for: Python, JavaScript/JSX, TypeScript/TSX, Rust, Go

Other file types use text-based analysis with graceful fallback.

File Size Handling

Size	Strategy
< 50MB	Full memory loading with AST caching
50-500MB	Memory-mapped access
> 500MB	Streaming (tail/head modes recommended)

Configuration

Environment variables for tuning:

LARGEFILE_MEMORY_THRESHOLD_MB=50      # RAM loading limit
LARGEFILE_MMAP_THRESHOLD_MB=500       # Memory mapping limit
LARGEFILE_FUZZY_THRESHOLD=0.8         # Match sensitivity (0.0-1.0)
LARGEFILE_MAX_SEARCH_RESULTS=20       # Results per search
LARGEFILE_BACKUP_DIR=~/.largefile/backups

Documentation

• API Reference - Detailed tool documentation
• Configuration Guide - All environment variables
• Examples - More workflow examples
• Design Document - Architecture details
• Contributing - Development setup