Large File MCP Server

Large File MCP Server

A Model Context Protocol (MCP) server for intelligent handling of large files with smart chunking, navigation, and streaming capabilities.

📚 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:

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

Development

Building from Source

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

Development Mode

npm run dev    # Watch mode
npm run lint   # Run linter
npm 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

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.