Binassistmcp

🚀 BinAssistMCP

BinAssistMCP serves as a comprehensive Model Context Protocol (MCP) server for Binary Ninja, integrating AI capabilities to empower reverse engineering.

🚀 Quick Start

BinAssistMCP is a powerful bridge between Binary Ninja and Large Language Models (LLMs) like Claude, providing comprehensive reverse engineering tools through the Model Context Protocol (MCP). It enables AI-assisted binary analysis by exposing Binary Ninja's advanced capabilities through both Server-Sent Events (SSE) and STDIO transports.

✨ Features

• Dual Transport Support: Both SSE (web-based) and STDIO (command-line) transports
• 40+ Analysis Tools: Complete Binary Ninja API wrapper with advanced functionality
• Multi-Binary Sessions: Concurrent analysis of multiple binaries with intelligent context management
• Smart Symbol Management: Advanced function searching, renaming, and type management
• Auto-Integration: Seamless Binary Ninja plugin with automatic startup capabilities
• Flexible Configuration: Comprehensive settings management through Binary Ninja's interface
• AI-Ready: Optimized for LLM integration with structured tool responses

Use Cases

• AI-Assisted Reverse Engineering: Leverage LLMs for intelligent code analysis and documentation
• Automated Binary Analysis: Script complex analysis workflows with natural language
• Research and Education: Teach reverse engineering concepts with AI guidance
• Security Analysis: Accelerate vulnerability research and malware analysis
• Code Understanding: Generate comprehensive documentation and explanations

📦 Installation

Prerequisites

• Binary Ninja: Version 4000 or higher
• Python: 3.8+ (typically bundled with Binary Ninja)
• Platform: Windows, macOS, or Linux

Option 1: Binary Ninja Plugin Manager (Recommended)

1. Open Binary Ninja
2. Navigate to Tools → Manage Plugins
3. Search for "BinAssistMCP"
4. Click Install
5. Restart Binary Ninja

Option 2: Manual Installation

Step 1: Download and Extract

git clone https://github.com/jtang613/BinAssistMCP.git
cd BinAssistMCP

Step 2: Install Dependencies

# Install Python dependencies
pip install -r requirements.txt

# Or install individually:
pip install anyio>=4.0.0 hypercorn>=0.16.0 mcp>=1.0.0 trio>=0.27.0 pydantic>=2.0.0 pydantic-settings>=2.0.0 click>=8.0.0

Step 3: Copy to Plugin Directory

Windows:

copy BinAssistMCP "%APPDATA%\Binary Ninja\plugins\"

macOS:

cp -r BinAssistMCP ~/Library/Application\ Support/Binary\ Ninja/plugins/

Linux:

cp -r BinAssistMCP ~/.binaryninja/plugins/

Step 4: Verify Installation

1. Restart Binary Ninja
2. Open any binary file
3. Check Tools menu for "BinAssistMCP" submenu
4. Look for startup messages in the log panel

Configuration

Basic Setup

1. Open Binary Ninja Settings (Edit → Preferences)
2. Navigate to the binassistmcp section
3. Configure server settings:
   • Host: localhost (default)
   • Port: 9090 (default)
   • Transport: both (SSE + STDIO)

Advanced Configuration

# Environment variables (optional)
export BINASSISTMCP_SERVER__HOST=localhost
export BINASSISTMCP_SERVER__PORT=9090
export BINASSISTMCP_SERVER__TRANSPORT=both
export BINASSISTMCP_BINARY__MAX_BINARIES=10

💻 Usage Examples

Starting the Server

Via Binary Ninja Menu:

1. Tools → BinAssistMCP → Start Server
2. Check log panel for startup confirmation
3. Note the server URL (e.g., http://localhost:9090)

Auto-Startup (Default):

• Server starts automatically when Binary Ninja loads a file
• Configurable via settings: binassistmcp.plugin.auto_startup

Connecting with Claude Desktop

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "binassist": {
      "command": "python",
      "args": ["/path/to/BinAssistMCP"],
      "env": {
        "BINASSISTMCP_SERVER__TRANSPORT": "stdio"
      }
    }
  }
}

Using with SSE Transport

Connect web-based MCP clients to:

http://localhost:9090/sse

Integration Examples

Basic Function Analysis

Ask Claude: "Analyze the main function in the loaded binary and explain what it does"

Claude will use tools like:
- get_functions() to find main
- decompile_function() to get readable code
- get_function_pseudo_c() for C representation
- analyze_function() for comprehensive analysis

Vulnerability Research

Ask Claude: "Find all functions that handle user input and check for buffer overflows"

Claude will use:
- search_functions_advanced() to find input handlers
- get_cross_references() to trace data flow
- get_variables() to analyze buffer usage
- set_comment() to document findings

📚 Documentation

Tool Details

BinAssistMCP provides over 40 specialized tools organized into functional categories:

Binary Management

• list_binaries - List all loaded binary files
• get_binary_status - Check analysis status and metadata
• update_analysis_and_wait - Force analysis update and wait for completion

Code Analysis & Decompilation

• decompile_function - Generate high-level decompiled code
• get_function_pseudo_c - Extract pseudo-C representation
• get_function_high_level_il - Access High-Level Intermediate Language
• get_function_medium_level_il - Access Medium-Level Intermediate Language
• get_disassembly - Retrieve assembly code with annotations

Information Retrieval

• get_functions - List all functions with metadata
• search_functions_by_name - Find functions by name patterns
• get_functions_advanced - Advanced filtering (size, complexity, parameters)
• search_functions_advanced - Multi-target searching (name, comments, calls, variables)
• get_function_statistics - Comprehensive binary statistics
• get_imports - Import table analysis grouped by module
• get_exports - Export table with symbol information
• get_strings - String extraction with context
• get_segments - Memory layout analysis
• get_sections - Binary section information

Symbol & Naming Management

• rename_symbol - Rename functions and data variables
• get_cross_references - Find all references to/from symbols
• analyze_function - Comprehensive function analysis
• get_call_graph - Call relationship mapping

Documentation & Comments

• set_comment - Add comments to specific addresses
• get_comment - Retrieve comments at addresses
• get_all_comments - Export all comments with context
• remove_comment - Delete existing comments
• set_function_comment - Add function-level documentation

Variable Management

• create_variable - Define local variables in functions
• get_variables - List function parameters and locals
• rename_variable - Rename variables for clarity
• set_variable_type - Update variable type information

Type System Management

• create_type - Define custom types and structures
• get_types - List all user-defined types
• create_enum - Create enumeration types
• create_typedef - Create type aliases
• get_type_info - Detailed type information
• get_classes - List classes and structures
• create_class - Define new classes/structures
• add_class_member - Add members to existing types

Data Analysis

• create_data_var - Define data variables at addresses
• get_data_vars - List all defined data variables
• get_data_at_address - Analyze raw data with type inference

Navigation & Context

• get_current_address - Get current cursor position
• get_current_function - Identify function at current address
• get_namespaces - Namespace and symbol organization

Advanced Analysis

• get_triage_summary - Complete binary overview
• get_function_statistics - Statistical analysis of all functions

Each tool is designed for seamless integration with AI workflows, providing structured responses that LLMs can easily interpret and act upon.

Troubleshooting

Common Issues

Server won't start:

• Check Binary Ninja log panel for error messages
• Verify all dependencies are installed
• Ensure port 9090 is not in use

Binary Ninja crashes:

• Check Python environment compatibility
• Try reducing max_binaries setting
• Restart with a single binary file

Tools return errors:

• Ensure binary analysis is complete
• Check if Binary Ninja file is still open
• Verify function/address exists

Support

• Issues: Report bugs on GitHub Issues
• Binary Ninja: Check official Binary Ninja documentation

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes following the existing code style
4. Test with multiple binary types
5. Submit a pull request

📄 License

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