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

Read the file to get current content
Extract the relevant section (50-500 lines with context)
Call FastApply API with partial content
Smart match finds the section in the full file
Replace the section atomically
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

Install LM Studio from https://lmstudio.ai
Download a FastApply-compatible model
Start the local server (default: http://localhost:1234)
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:

Do one thing well - Focus on efficient file editing
Trust the client - MCP client handles undo, concurrency, etc.
Optimize for common case - Partial editing is 10x more efficient
Clear errors - Help users fix problems quickly
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:

Fork the repository and create a feature branch
Write tests for new functionality
Ensure code meets quality standards
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

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

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

Read the file to get current content
Extract the relevant section (50-500 lines with context)
Call FastApply API with partial content
Smart match finds the section in the full file
Replace the section atomically
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

Install LM Studio from https://lmstudio.ai
Download a FastApply-compatible model
Start the local server (default: http://localhost:1234)
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:

Do one thing well - Focus on efficient file editing
Trust the client - MCP client handles undo, concurrency, etc.
Optimize for common case - Partial editing is 10x more efficient
Clear errors - Help users fix problems quickly
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:

Fork the repository and create a feature branch
Write tests for new functionality
Ensure code meets quality standards
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