fastapply-mcp 1.1.1

MCP server for AI-powered code editing through FastApply integration

FastApply MCP Server

A streamlined Model Context Protocol server for efficient AI-powered code editing using FastApply. Inspired by opencode-fast-apply's simplicity and partial editing approach.

Overview

FastApply MCP Server provides intelligent code editing through partial file editing, achieving 80-98% token savings compared to full-file approaches. The server uses smart matching to locate and replace code sections automatically, making it ideal for editing large files efficiently.

Key Features

• Partial File Editing: Edit only the sections you need (50-500 lines recommended)
• Smart Matching: Automatic exact and normalized whitespace matching
• XML Safety: Built-in protection against prompt injection
• Token Efficiency: 80-98% token savings vs full-file editing
• Binary Detection: Automatic detection and rejection of binary files
• Atomic Operations: Safe file writes with automatic rollback on failure
• Clear Error Messages: Actionable suggestions for troubleshooting

Installation

Requirements

• Python 3.13 or higher
• FastApply-compatible server (LM Studio, Ollama, or OpenAI-compatible endpoint)

Using uvx (Recommended)

Run directly without installation:

uvx fastapply-mcp

Manual Installation

git clone https://github.com/your-org/fastapply-mcp.git
cd fastapply-mcp

# Using uv
uv sync
source .venv/bin/activate
uv pip install -e .

# Or using pip
pip install -e .

Configuration

Configure the server with just 3 environment variables:

# .env file
FAST_APPLY_URL=http://localhost:1234/v1
FAST_APPLY_MODEL=fastapply-1.5b
FAST_APPLY_API_KEY=optional-api-key

That's it! No complex configuration needed.

MCP Integration

Claude Desktop

Add to your Claude Desktop configuration:

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

Using uvx (Recommended)

{
  "mcpServers": {
    "fastapply": {
      "command": "uvx",
      "args": ["fastapply-mcp"],
      "env": {
        "FAST_APPLY_URL": "http://localhost:1234/v1",
        "FAST_APPLY_MODEL": "fastapply-1.5b"
      }
    }
  }
}

Manual Installation

{
  "mcpServers": {
    "fastapply": {
      "command": "python",
      "args": ["/path/to/fastapply-mcp/src/fastapply_mcp/main.py"],
      "env": {
        "FAST_APPLY_URL": "http://localhost:1234/v1",
        "FAST_APPLY_MODEL": "fastapply-1.5b"
      }
    }
  }
}

Other MCP Clients

The server implements the standard MCP protocol and works with any compatible client.

Tool: fast_apply_edit

The server provides a single, focused tool for efficient code editing.

Parameters

• target_filepath (required): Path to the file to edit (relative or absolute)
• original_code (required): The exact section of code to modify (50-500 lines recommended)
• code_edit (required): The changes to apply

How It Works

1. Read the file to get current content
2. Extract the relevant section (50-500 lines with context)
3. Call FastApply API with partial content
4. Smart match finds the section in the full file
5. Replace the section atomically
6. Generate diff for verification

Example Usage

{
  "target_filepath": "src/utils.py",
  "original_code": "def parse_config(path):\n    with open(path) as f:\n        return json.load(f)",
  "code_edit": "def parse_config(path):\n    try:\n        with open(path) as f:\n            return json.load(f)\n    except FileNotFoundError:\n        raise ConfigError(f'Config not found: {path}')"
}

Lazy Edit Markers

Use ... existing code ... markers for unchanged sections:

# ... existing code ...
def updated_function():
    return "modified"
# ... existing code ...

This tells the AI to skip regenerating unchanged parts, making edits faster.

Token Efficiency

Partial editing provides massive token savings:

File Size	Full File	Partial (100 lines)	Savings
100 lines	2,500 tokens	500 tokens	80%
500 lines	12,500 tokens	1,000 tokens	92%
1000 lines	25,000 tokens	1,500 tokens	94%
5000 lines	125,000 tokens	2,000 tokens	98%

Smart Matching

The tool uses a two-tier matching system:

1. Exact Match (Priority)

Finds exact string match in the file.

2. Normalized Match (Fallback)

Handles CRLF/LF differences automatically:

• Normalizes \r\n → \n
• Normalizes \r → \n
• Matches whitespace-normalized content

3. Uniqueness Check

Ensures the section appears only once in the file to prevent ambiguous replacements.

XML Safety

Built-in protection against prompt injection:

# User code with XML tags
original_code = "<code>malicious</code>"

# Automatically escaped before API call
# "&lt;code&gt;malicious&lt;/code&gt;"

# Safely processed and unescaped after

All XML special characters (&, <, >, ", ') are automatically escaped and unescaped.

FastApply Backend Options

LM Studio

1. Install LM Studio from https://lmstudio.ai
2. Download a FastApply-compatible model
3. Start the local server (default: http://localhost:1234)
4. Configure FAST_APPLY_URL=http://localhost:1234/v1

Ollama

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a FastApply model
ollama pull fastapply-1.5b

# Start the server
ollama serve

Configure FAST_APPLY_URL=http://localhost:11434/v1

OpenAI or Custom Servers

Any OpenAI-compatible API works:

FAST_APPLY_URL=https://api.openai.com/v1
FAST_APPLY_MODEL=gpt-4
FAST_APPLY_API_KEY=sk-...

Security

• Workspace Isolation: All operations confined to current working directory
• Path Validation: Prevents directory traversal attacks
• File Size Limits: 10MB default maximum
• Binary Detection: Rejects binary files automatically
• UTF-8 Validation: Ensures proper file encoding
• Atomic Writes: Safe file operations with rollback

Error Handling

Clear, actionable error messages:

❌ Error: Cannot locate original_code in file (whitespace mismatch detected).

💡 Troubleshooting:
  1. Re-read the file to get current content
  2. Ensure original_code matches exactly (including whitespace)
  3. Provide more context to make the section unique

Troubleshooting

Connection Issues

Verify your FastApply server is running:

curl http://localhost:1234/v1/models

File Not Found

Use the tool only for existing files. For new files, use your MCP client's write tool.

Cannot Locate Section

• Re-read the file to get current content
• Ensure whitespace matches exactly (tabs vs spaces)
• Provide more context to make the section unique

Whitespace Mismatch

The tool handles CRLF/LF differences automatically, but tabs vs spaces must match exactly.

Development

Project Structure

fastapply-mcp/
├── src/
│   └── fastapply_mcp/
│       ├── __init__.py
│       └── main.py          # Single-file implementation (~487 lines)
├── .env.example
├── pyproject.toml
└── README.md

Code Quality

# Format code
ruff format .

# Lint code
ruff check .

# Type checking
mypy src/

# Syntax check
python -m py_compile src/fastapply_mcp/main.py

Design Philosophy

This implementation follows these principles:

1. Do one thing well - Focus on efficient file editing
2. Trust the client - MCP client handles undo, concurrency, etc.
3. Optimize for common case - Partial editing is 10x more efficient
4. Clear errors - Help users fix problems quickly
5. No premature optimization - Remove unused features

Performance

Original Approach (Full File)

• Read: 5000 lines
• Send to API: 125,000 tokens
• Process: ~30 seconds
• Cost: High

Simplified Approach (Partial)

• Read: 5000 lines (send only 100)
• Send to API: 2,000 tokens
• Process: ~3 seconds
• Cost: 98% cheaper

Contributing

Contributions are welcome! Please:

1. Fork the repository and create a feature branch
2. Write tests for new functionality
3. Ensure code meets quality standards
4. Submit a pull request with clear description

License

MIT License - see LICENSE file for details.

Acknowledgments

• Inspiration: opencode-fast-apply for the partial editing approach
• MCP Community: For the Model Context Protocol specification
• FastApply: For the efficient code merging models

Support

• GitHub Issues: Report bugs and request features
• Discussions: Ask questions and share ideas
• Documentation: See inline code comments for implementation details