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@willianpinho/large-file-mcp

v1.1.1

Published

MCP server for intelligent handling of large files — smart chunking, search, navigation, and streaming.

Downloads

340

Readme

Large File MCP Server

MCP server for intelligent handling of large files — smart chunking, search, navigation, and streaming.

npm version npm downloads CI codecov License: MIT TypeScript Node.js MCP Documentation GitHub stars GitHub issues

📚 Full Documentation | API Reference | Examples

Features

  • Smart Chunking - Automatically determines optimal chunk size based on file type
  • Intelligent Navigation - Jump to specific lines with surrounding context
  • Powerful Search - Regex support with context lines before/after matches
  • File Analysis - Comprehensive metadata and statistical analysis
  • Memory Efficient - Stream files of any size without loading into memory
  • Performance Optimized - Built-in LRU caching for frequently accessed chunks
  • Type Safe - Written in TypeScript with strict typing
  • Cross-Platform - Works on Windows, macOS, and Linux

Installation

npm install -g @willianpinho/large-file-mcp

Or use directly with npx:

npx @willianpinho/large-file-mcp

Quick Start

Claude Code CLI

Add the MCP server using the CLI:

# Add for current project only (local scope)
claude mcp add --transport stdio --scope local large-file-mcp -- npx -y @willianpinho/large-file-mcp

# Add globally for all projects (user scope)
claude mcp add --transport stdio --scope user large-file-mcp -- npx -y @willianpinho/large-file-mcp

Verify installation:

claude mcp list
claude mcp get large-file-mcp

Remove if needed:

# Remove from local scope
claude mcp remove large-file-mcp -s local

# Remove from user scope
claude mcp remove large-file-mcp -s user

MCP Scopes:

  • local - Available only in the current project directory
  • user - Available globally for all projects
  • project - Defined in .mcp.json for team sharing

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "large-file": {
      "command": "npx",
      "args": ["-y", "@willianpinho/large-file-mcp"]
    }
  }
}

Config file locations:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Restart Claude Desktop after editing.

Other AI Platforms

Gemini:

{
  "tools": [
    {
      "name": "large-file-mcp",
      "command": "npx @willianpinho/large-file-mcp",
      "protocol": "mcp"
    }
  ]
}

Usage

Once configured, you can use natural language to interact with large files:

Read the first chunk of /var/log/system.log
Find all ERROR messages in /var/log/app.log
Show me line 1234 of /code/app.ts with context
Get the structure of /data/sales.csv

Available Tools

read_large_file_chunk

Read a specific chunk of a large file with intelligent chunking.

Parameters:

  • filePath (required): Absolute path to the file
  • chunkIndex (optional): Zero-based chunk index (default: 0)
  • linesPerChunk (optional): Lines per chunk (auto-detected if not provided)
  • includeLineNumbers (optional): Include line numbers (default: false)

Example:

{
  "filePath": "/var/log/system.log",
  "chunkIndex": 0,
  "includeLineNumbers": true
}

search_in_large_file

Search for patterns in large files with context.

Parameters:

  • filePath (required): Absolute path to the file
  • pattern (required): Search pattern
  • caseSensitive (optional): Case sensitive search (default: false)
  • regex (optional): Use regex pattern (default: false)
  • maxResults (optional): Maximum results (default: 100)
  • contextBefore (optional): Context lines before match (default: 2)
  • contextAfter (optional): Context lines after match (default: 2)

Example:

{
  "filePath": "/var/log/error.log",
  "pattern": "ERROR.*database",
  "regex": true,
  "maxResults": 50
}

get_file_structure

Analyze file structure and get comprehensive metadata.

Parameters:

  • filePath (required): Absolute path to the file

Returns: File metadata, line statistics, recommended chunk size, and sample lines.

navigate_to_line

Jump to a specific line with surrounding context.

Parameters:

  • filePath (required): Absolute path to the file
  • lineNumber (required): Line number to navigate to (1-indexed)
  • contextLines (optional): Context lines before/after (default: 5)

get_file_summary

Get comprehensive statistical summary of a file.

Parameters:

  • filePath (required): Absolute path to the file

Returns: File metadata, line statistics, character statistics, and word count.

stream_large_file

Stream a file in chunks for processing very large files.

Parameters:

  • filePath (required): Absolute path to the file
  • chunkSize (optional): Chunk size in bytes (default: 64KB)
  • startOffset (optional): Starting byte offset (default: 0)
  • maxChunks (optional): Maximum chunks to return (default: 10)

Supported File Types

The server intelligently detects and optimizes for:

  • Text files (.txt) - 500 lines/chunk
  • Log files (.log) - 500 lines/chunk
  • Code files (.ts, .js, .py, .java, .cpp, .go, .rs, etc.) - 300 lines/chunk
  • CSV files (.csv) - 1000 lines/chunk
  • JSON files (.json) - 100 lines/chunk
  • XML files (.xml) - 200 lines/chunk
  • Markdown files (.md) - 500 lines/chunk
  • Configuration files (.yml, .yaml, .sh, .bash) - 300 lines/chunk

Configuration

Customize behavior using environment variables:

| Variable | Description | Default | | --------------- | -------------------------- | --------- | | CHUNK_SIZE | Default lines per chunk | 500 | | OVERLAP_LINES | Overlap between chunks | 10 | | MAX_FILE_SIZE | Maximum file size in bytes | 10GB | | CACHE_SIZE | Cache size in bytes | 100MB | | CACHE_TTL | Cache TTL in milliseconds | 5 minutes | | CACHE_ENABLED | Enable/disable caching | true |

Example with custom settings (Claude Desktop):

{
  "mcpServers": {
    "large-file": {
      "command": "npx",
      "args": ["-y", "@willianpinho/large-file-mcp"],
      "env": {
        "CHUNK_SIZE": "1000",
        "CACHE_ENABLED": "true"
      }
    }
  }
}

Example with custom settings (Claude Code CLI):

claude mcp add --transport stdio --scope user large-file-mcp \
  --env CHUNK_SIZE=1000 \
  --env CACHE_ENABLED=true \
  -- npx -y @willianpinho/large-file-mcp

Examples

Analyzing Log Files

Analyze /var/log/nginx/access.log and find all 404 errors

The AI will use the search tool to find patterns and provide context around each match.

Code Navigation

Find all function definitions in /project/src/main.py

Uses regex search to locate function definitions with surrounding code context.

CSV Data Exploration

Show me the structure of /data/sales.csv

Returns metadata, line count, sample rows, and recommended chunk size.

Large File Processing

Stream the first 100MB of /data/huge_dataset.json

Uses streaming mode to handle very large files efficiently.

Performance

Caching

  • LRU Cache with configurable size (default 100MB)
  • TTL-based expiration (default 5 minutes)
  • 80-90% hit rate for repeated access
  • Significant performance improvement for frequently accessed files

Memory Management

  • Streaming architecture - files are read line-by-line, never fully loaded
  • Configurable chunk sizes - adjust based on your use case
  • Smart buffering - minimal memory footprint for search operations

File Size Handling

| File Size | Operation Time | Method | | --------- | -------------- | ----------------- | | < 1MB | < 100ms | Direct read | | 1-100MB | < 500ms | Streaming | | 100MB-1GB | 1-3s | Streaming + cache | | > 1GB | Progressive | AsyncGenerator |

Development

Building from Source

git clone https://github.com/willianpinho/large-file-mcp.git
cd large-file-mcp
pnpm install
pnpm build

Development Mode

pnpm dev    # Watch mode
pnpm lint   # Run linter
pnpm start  # Run server

Project Structure

src/
├── index.ts        # Entry point
├── server.ts       # MCP server implementation
├── fileHandler.ts  # Core file handling logic
├── cacheManager.ts # Caching implementation
└── types.ts        # TypeScript type definitions

Troubleshooting

File not accessible

Ensure the file path is absolute and the file has read permissions:

chmod +r /path/to/file

Out of memory

  1. Reduce CHUNK_SIZE environment variable
  2. Disable cache with CACHE_ENABLED=false
  3. Use stream_large_file for very large files

Slow search performance

  1. Reduce maxResults parameter
  2. Use startLine and endLine to limit search range
  3. Ensure caching is enabled

Claude Code CLI: MCP server not found

Check if the server is installed:

claude mcp list

If not listed, reinstall:

claude mcp add --transport stdio --scope user large-file-mcp -- npx -y @willianpinho/large-file-mcp

Check server health:

claude mcp get large-file-mcp

Configuration

The server is tunable through environment variables:

  • CACHE_ENABLED: Enable/disable caching (default: true)
  • CACHE_SIZE: Cache size in bytes (default: 104857600 - 100MB)
  • CACHE_TTL: Cache TTL in milliseconds (default: 300000 - 5 minutes)
  • CHUNK_SIZE: Default lines per chunk (default: 500)
  • MAX_FILE_SIZE: Maximum file size in bytes (default: 10737418240 - 10GB)
  • OVERLAP_LINES: Overlap between chunks (default: 10)

Designed For

  • Log Analysis: Processing multi-GB log files with search and navigation
  • Data Processing: Reading large CSV/JSON files in manageable chunks
  • Code Review: Navigating large codebases efficiently
  • System Monitoring: Analyzing system logs and debug outputs
  • Document Analysis: Processing large text documents

Compatible with Claude Code, Cursor, and Gemini CLI. Available on npm and the Glama.ai registry, and listed in community awesome-MCP directories.

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Development Workflow

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Ensure code builds and lints successfully
  5. Submit a pull request

See CONTRIBUTING.md for detailed guidelines.

License

MIT

Support

  • Issues: GitHub Issues
  • Documentation: This README and inline code documentation
  • Examples: Check the examples/ directory

Acknowledgments

Built with the Model Context Protocol SDK.


Made for the AI developer community.