Skip to main content
Glama

File Patch MCP Server

by shenning00
OverviewSchemaRelated ServersScoreDiscussions
Python
Local
MIT License
1

Patch MCP Server

CI License: MIT Python 3.10+ Code style: black Coverage

A Model Context Protocol (MCP) server that enables AI assistants to safely apply unified diff patches to files with comprehensive security validation.

Version: 2.0.0 | Status: Beta | Tools: 7 | Test Coverage: 84% (286 tests)

Why Patch MCP Server?

Enable your AI assistant to:

  • ✅ Apply code changes using standard unified diff format

  • ✅ Validate patches before applying them

  • ✅ Create and restore backups automatically

  • ✅ Revert changes safely if something goes wrong

  • ✅ Apply multiple changes atomically via multi-hunk patches

  • ✅ Test changes with dry-run mode before committing

All with built-in security (no symlinks, binary files, or directory traversal) and automatic rollback on failures.

Why Use Patch Tools Instead of Direct Editing?

For AI assistants and developers, apply_patch provides significant advantages over traditional Edit operations:

Feature

apply_patch

Edit Tool

Format

Standard unified diff (like git diff)

Custom old/new strings

Multiple changes

✅ Multi-hunk (atomic)

❌ Separate calls (no atomicity)

Change visibility

✅ Clear diff view

❌ Hard to spot differences

Token efficiency

✅ ~50% less tokens

❌ Full old+new strings required

Testing

✅ Dry-run mode available

❌ No preview capability

Atomicity

✅ All changes succeed/fail together

❌ Partial updates possible

Reviewability

✅ Standard format developers know

❌ Custom format

Real-World Example

Task: Update 3 config values in one file

  • Edit: 3 separate tool calls, no atomicity, hard to review

  • apply_patch: 1 call with 3 hunks, atomic operation, clear diff

Bottom line: For most file modifications, apply_patch is more efficient, safer, and clearer than Edit operations.

Quick Start

Installation

# Clone the repository git clone https://github.com/shenning00/patch_mcp.git cd patch_mcp # Create virtual environment and install python3 -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate pip install -e ".[dev]"

Configure with Claude Desktop

Add to your Claude Desktop MCP configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{ "mcpServers": { "patch": { "command": "python", "args": ["-m", "patch_mcp"], "cwd": "/path/to/patch_mcp" } } }

Restart Claude Desktop and the patch tools will be available.

Run Standalone

python -m patch_mcp

The server runs in stdio mode and communicates via the Model Context Protocol.

Available Tools

The server provides 7 tools for comprehensive patch management:

Core Patch Operations

  1. apply_patch - Apply a unified diff patch to a file

    • Supports multi-hunk patches (apply multiple changes atomically)

    • Dry-run mode for testing without modification

    • Automatic validation before application

  2. validate_patch - Check if a patch can be applied (read-only)

    • Preview changes before applying

    • Detect context mismatches

    • See affected line ranges

  3. revert_patch - Reverse a previously applied patch

    • Undo changes safely

    • Works with multi-hunk patches

    • Requires exact original patch

  4. generate_patch - Create a patch from two file versions

    • Compare original and modified files

    • Generate standard unified diff format

    • Configurable context lines

Analysis & Inspection

  1. inspect_patch - Analyze patch content without files

    • See what files are affected

    • Count hunks and line changes

    • Supports multi-file patches

Backup & Recovery

  1. backup_file - Create timestamped backups

    • Format: filename.backup.YYYYMMDD_HHMMSS

    • Preserves file metadata

    • Automatic disk space checks

  2. restore_backup - Restore from backups

    • Auto-detect original location

    • Safety checks before overwriting

    • Force option available

Example: How an AI Assistant Uses This Server

Scenario 1: Simple Code Modification

AI Assistant's thought process:

"The user wants to change the timeout from 30 to 60 seconds in config.py. I'll use the patch server to do this safely."

AI uses tools:

  1. Generate the patch:

Tool: generate_patch Args: { "original_file": "config.py", "modified_file": "config_new.py" }

  1. Validate it can be applied:

Tool: validate_patch Args: { "file_path": "config.py", "patch": "--- config.py\n+++ config.py\n@@ -10,3 +10,3 @@\n-timeout = 30\n+timeout = 60" } Result: { "success": true, "can_apply": true, "preview": {"lines_to_add": 1, "lines_to_remove": 1} }

  1. Create backup before applying:

Tool: backup_file Args: {"file_path": "config.py"} Result: {"backup_file": "config.py.backup.20250118_143052"}

  1. Apply the patch:

Tool: apply_patch Args: { "file_path": "config.py", "patch": "--- config.py\n+++ config.py\n@@ -10,3 +10,3 @@\n-timeout = 30\n+timeout = 60" } Result: {"success": true, "applied": true}

AI reports to user:

"I've updated the timeout from 30 to 60 seconds in config.py. A backup was created at config.py.backup.20250118_143052 in case you need to revert."

Scenario 2: Multi-file Refactoring with Dry Run

AI Assistant's thought process:

"The user wants to rename a function across 3 files. I should test this first with dry-run, then apply atomically."

AI uses tools:

  1. Test each patch with dry-run:

Tool: apply_patch Args: { "file_path": "utils.py", "patch": "...", "dry_run": true } Result: {"success": true, "changes": {"lines_added": 5, "lines_removed": 5}}

  1. Apply all patches:

Applies each patch with backups and validation

AI reports to user:

"I've renamed the function across utils.py, handlers.py, and tests.py. All changes were validated first and applied atomically."

Scenario 3: Something Goes Wrong

AI Assistant's thought process:

"The patch failed to apply. I should revert to the backup."

AI uses tools:

Tool: restore_backup Args: { "backup_file": "config.py.backup.20250118_143052" } Result: {"success": true, "restored_to": "config.py"}

AI reports to user:

"The patch couldn't be applied because the file content didn't match. I've restored the original file from backup. The file may have been modified since we discussed the change."

Security Features

All operations include comprehensive security checks:

  • 🔒 Symlink Protection - Symlinks are rejected (security policy)

  • 🔒 Binary File Detection - Binary files automatically detected and rejected

  • 🔒 Size Limits - Maximum 10MB file size

  • 🔒 Disk Space Validation - Ensures 100MB+ free space before operations

  • 🔒 Path Traversal Protection - Prevents directory escaping

  • 🔒 Permission Checks - Validates read/write permissions

  • 🔒 Atomic Operations - File replacements use atomic rename

See SECURITY.md for detailed security information.

Multi-Hunk Patches

A powerful feature: apply multiple changes to different parts of a file atomically in a single patch:

--- config.py +++ config.py @@ -10,3 +10,3 @@ # Connection settings -timeout = 30 +timeout = 60 @@ -25,3 +25,3 @@ # Retry settings -retries = 3 +retries = 5 @@ -50,3 +50,3 @@ # Debug settings -debug = False +debug = True

All three changes are applied together or none are applied. If any hunk fails, the entire patch is rejected.

Documentation

Documentation

Error Types

The server provides 10 distinct error types for precise error handling:

Standard Errors:

  • file_not_found, permission_denied, invalid_patch, context_mismatch, encoding_error, io_error

Security Errors:

  • symlink_error, binary_file, disk_space_error, resource_limit

Testing & Quality

  • 286 tests (all passing)

  • 84% code coverage across all modules

  • Strict type checking with mypy

  • Code formatting with black

  • Linting with ruff

  • CI/CD via GitHub Actions (Linux, macOS, Windows)

# Run tests pytest tests/ -v --cov=src/patch_mcp # Check code quality black src/patch_mcp tests/ ruff check src/patch_mcp tests/ mypy src/patch_mcp --strict

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for:

  • Development setup

  • Testing guidelines

  • Code quality standards

  • Commit message conventions

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author: Scott Henning

Support

Model Context Protocol

This server implements the Model Context Protocol (MCP), an open protocol that enables AI assistants to securely interact with local tools and data sources.

Learn more:

Last Updated: 2025-10-19 | Phase: 5 of 5 (Beta) | Tools: 7/7

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

Resources

New MCP Servers

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/shenning00/patch_mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server