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Willian Pinho
Willian Pinho

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Large File MCP: Handle Massive Files in Claude with Intelligent Chunking

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Large File MCP: Handle Massive Files in Claude with Intelligent Chunking

Have you ever tried to analyze a 500MB log file with Claude only to hit token limits? Or struggled to navigate through a massive CSV dataset? I built Large File MCP to solve exactly these problems.

The Problem with Large Files in AI Assistants

AI assistants like Claude Desktop are incredibly powerful, but they have a fundamental limitation: token context windows. When you're dealing with:

  • Multi-gigabyte log files from production servers
  • Large CSV datasets with millions of rows
  • Massive JSON configuration files
  • Extensive codebases spanning thousands of lines

...traditional file reading approaches fail. You can't just load everything into memory, and manually chunking files is tedious and error-prone.

Introducing Large File MCP

Large File MCP is a Model Context Protocol (MCP) server that provides intelligent, production-ready large file handling for AI assistants. It's designed to make working with files of any size as seamless as working with small text files.

Key Features

Smart Chunking
The server automatically detects your file type and applies optimal chunking strategies:

  • Text/log files: 500 lines per chunk
  • Code files (.ts, .py, .java): 300 lines per chunk
  • CSV files: 1000 lines per chunk
  • JSON files: 100 lines per chunk

Intelligent Navigation
Jump to any line in a file with surrounding context:

Show me line 1234 of /var/log/system.log with context
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Powerful Search
Find patterns with regex support and contextual results:

Find all ERROR messages in /var/log/app.log
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Memory Efficient
Files are streamed line-by-line, never fully loaded into memory. Built-in LRU caching provides 80-90% hit rates for frequently accessed files.

Production Ready

  • 91.8% test coverage
  • Cross-platform (Windows, macOS, Linux)
  • Type-safe TypeScript implementation
  • Comprehensive documentation

Installation

Installing Large File MCP is straightforward. Choose your preferred method:

Claude Desktop (Recommended)

Add to your claude_desktop_config.json:

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

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

Restart Claude Desktop after editing.

Claude Code CLI 

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

# Verify installation
claude mcp list
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npm Global Install 

npm install -g @willianpinho/large-file-mcp
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Real-World Use Cases

Let me show you how Large File MCP transforms common workflows:

1. Analyzing Production Logs

Scenario: You need to debug a production issue buried in a 2GB nginx log file.

With Large File MCP:

Find all 500 errors in /var/log/nginx/access.log from the last hour
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The AI assistant will:

  1. Use search_in_large_file with regex pattern
  2. Return matching lines with context
  3. Stream results efficiently without loading the entire file

Result: Found 47 errors in ~3 seconds, with full context for each match.

2. Code Navigation in Large Codebases

Scenario: Understanding function definitions in a 10,000-line Python file.

With Large File MCP:

Find all class definitions in /project/src/main.py and explain their purpose
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The server uses intelligent chunking to:

  • Navigate to each class definition
  • Provide surrounding context
  • Cache frequently accessed code sections

Result: Instant navigation to any part of the codebase with full context.

3. CSV Data Exploration

Scenario: Analyzing a 500MB sales dataset with 5 million rows.

With Large File MCP:

Show me the structure of /data/sales.csv and find all transactions over $10,000
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The AI uses:

  1. get_file_structure - Get metadata and sample rows
  2. search_in_large_file - Find high-value transactions
  3. Smart chunking - Process 1000 rows at a time

Result: Comprehensive analysis without loading 500MB into memory.

4. Streaming Very Large Files

Scenario: Processing a 5GB JSON dataset that exceeds memory limits.

With Large File MCP:

Stream the first 100MB of /data/huge_dataset.json
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Uses stream_large_file with:

  • Configurable chunk sizes (default 64KB)
  • Starting offset support
  • Maximum chunk limits

Result: Progressive processing of massive files with minimal memory footprint.

Available Tools

Large File MCP provides 6 powerful tools:

1. read_large_file_chunk

Read specific chunks with intelligent sizing:

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

Regex search with context:

{
  "filePath": "/var/log/error.log",
  "pattern": "ERROR.*database",
  "regex": true,
  "contextBefore": 2,
  "contextAfter": 2
}
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3. navigate_to_line

Jump to specific lines:

{
  "filePath": "/code/app.ts",
  "lineNumber": 1234,
  "contextLines": 5
}
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4. get_file_structure

Comprehensive file analysis:

{
  "filePath": "/data/sales.csv"
}
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5. get_file_summary

Statistical summary:

{
  "filePath": "/data/report.txt"
}
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6. stream_large_file

Stream files progressively:

{
  "filePath": "/data/huge_file.json",
  "chunkSize": 65536,
  "maxChunks": 10
}
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Performance Benchmarks

Here's how Large File MCP performs across different file sizes:

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

Caching Performance:

  • LRU Cache: 100MB default size
  • TTL: 5 minutes
  • Cache hit rate: 80-90% for repeated access
  • Significant speedup for frequently accessed files

Configuration & Customization

Fine-tune behavior using environment variables:

{
  "mcpServers": {
    "large-file": {
      "command": "npx",
      "args": ["-y", "@willianpinho/large-file-mcp"],
      "env": {
        "CHUNK_SIZE": "1000",
        "CACHE_ENABLED": "true",
        "CACHE_SIZE": "209715200",
        "MAX_FILE_SIZE": "10737418240"
      }
    }
  }
}
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Available Options:

Variable Description Default
CHUNK_SIZE Lines per chunk 500
OVERLAP_LINES Chunk overlap 10
MAX_FILE_SIZE Max file size 10GB
CACHE_SIZE Cache size 100MB
CACHE_TTL Cache TTL 5min

Why MCP?

The Model Context Protocol (MCP) is an open protocol that standardizes how AI assistants interact with external tools and data sources. Benefits include:

  • Universal Compatibility: Works with Claude Desktop, Claude Code CLI, and other MCP-compatible clients
  • Security: Sandboxed execution with explicit permissions
  • Extensibility: Easy to integrate with other MCP servers
  • Standardization: One implementation works everywhere

Production-Ready Quality

Large File MCP is built to production standards:

  • 91.8% Test Coverage - Comprehensive test suite with Jest
  • Type Safety - Written in TypeScript with strict typing
  • CI/CD - Automated testing and deployment
  • Documentation - Complete docs at willianpinho.github.io/large-file-mcp
  • Active Maintenance - Regular updates and bug fixes

Getting Started

Ready to handle large files effortlessly?

  1. Install the server: 
   claude mcp add --transport stdio --scope user large-file-mcp -- npx -y @willianpinho/large-file-mcp
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  1. Verify installation: 
   claude mcp list
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  1. Start using it: Open Claude and try: 
   Analyze /var/log/system.log and find all errors
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Links & Resources

Conclusion

Large File MCP transforms how you work with massive files in AI assistants. Whether you're debugging production logs, analyzing datasets, or navigating large codebases, intelligent chunking and streaming make it seamless.

The combination of smart chunking, powerful search, efficient caching, and production-ready quality makes it an essential tool for developers working with large files.

Give it a try and let me know what you think! Star the project on GitHub if you find it useful.

What large file challenges are you facing? Share your use cases in the comments!

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