MCP Browser

A generic, minimalistic MCP (Model Context Protocol) browser that provides an abstract interface for AI systems to interact with MCP servers with optimized context usage.

Overview

MCP Browser acts as a smart proxy between AI systems and MCP servers, providing:

• Generic JSON-RPC interface: Single call() method for all operations
• Context optimization: Sparse mode to minimize initial tool exposure
• Tool discovery: Dynamic exploration of available tools via JSONPath
• Automatic routing: Transparent routing to appropriate MCP servers
• Built-in servers: Automatically starts useful MCP servers (screen, memory, patterns, onboarding)

Key Features

1. Minimalistic API

   • call(jsonrpc_object): Execute any JSON-RPC call
   • discover(jsonpath): Explore available tools and their schemas
   • onboarding(identity): Get/set identity-specific instructions

2. Context Optimization

   • Only exposes 3 essential tools initially in sparse mode
   • Tools are loaded on-demand to minimize context usage
   • Full tool descriptions cached but not exposed until needed

3. Generic Design

   • Protocol-agnostic (works with any MCP server)
   • No hardcoded tool knowledge
   • Configuration-driven server management

4. Built-in Servers

   • Screen: GNU screen session management for persistent processes
   • Memory: Project memory, tasks, decisions, and knowledge management
   • Patterns: Auto-response pattern management for automation
   • Onboarding: Identity-aware onboarding for AI contexts

Architecture

mcp-browser/
├── mcp_browser/
│   ├── __init__.py
│   ├── proxy.py          # Main MCP proxy
│   ├── server.py         # MCP server management
│   ├── multi_server.py   # Multi-server manager
│   ├── registry.py       # Tool registry and discovery
│   ├── filter.py         # Message filtering and sparse mode
│   ├── buffer.py         # JSON-RPC message buffering
│   └── config.py         # Configuration management
├── mcp_servers/          # Built-in MCP servers
│   ├── base.py           # Base server implementation
│   ├── screen/           # Screen session management
│   ├── memory/           # Memory and context management
│   ├── pattern_manager/  # Pattern automation
│   └── onboarding/       # Identity-aware onboarding
├── tests/
├── docs/
│   ├── DESIGN.md         # Architecture and design details
│   ├── WORKING_DIRECTORIES.md  # Working directory and process guide
│   └── ...
└── config/
    └── default.yaml      # Default configuration

Important: See docs/WORKING_DIRECTORIES.md for detailed information about working directories, process architecture, and file path handling.

Installation

# Clone the repository
git clone https://github.com/Xilope0/mcp-browser.git
cd mcp-browser

# Install in development mode
pip install -e .

# Or install directly from GitHub
pip install git+https://github.com/Xilope0/mcp-browser.git

Quick Start

# Run interactive mode
mcp-browser

# Run as MCP server (for chaining)
mcp-browser --mode server

# Connect to specific MCP server
mcp-browser --server claude-code

# Generate AI documentation
python setup.py aidocs

Usage

from mcp_browser import MCPBrowser

# Initialize browser (built-in servers start automatically)
async with MCPBrowser() as browser:
    # Execute any JSON-RPC call
    response = await browser.call({
        "jsonrpc": "2.0",
        "id": 1,
        "method": "tools/list",
        "params": {}
    })
    
    # Discover tool details
    tool_info = browser.discover("$.tools[?(@.name=='Bash')]")
    
    # Use identity-aware onboarding
    response = await browser.call({
        "jsonrpc": "2.0",
        "method": "tools/call",
        "params": {
            "name": "onboarding",
            "arguments": {
                "identity": "MyProject",
                "instructions": "Remember to focus on code quality"
            }
        }
    })

Sparse Mode

In sparse mode (default), only 3 tools are initially visible:

1. mcp_discover: Explore available tools using JSONPath
2. mcp_call: Execute any tool by name
3. onboarding: Get/set identity-specific instructions

All other tools (potentially hundreds) are hidden but fully accessible through these meta-tools.

Design Principles

1. Generic: No tool-specific knowledge built into the browser
2. Minimal: Smallest possible API surface
3. Efficient: Optimized for minimal context usage
4. Transparent: Acts as a pass-through proxy with intelligent enhancements

Contributing

This project is developed exclusively by AI assistants. See CLAUDE.md for details about the AI-only development process.

If you're an AI assistant working on this project:

1. Read CLAUDE.md for development guidelines
2. Generate documentation with python setup.py aidocs
3. Test changes with the example scripts
4. Maintain context efficiency as the primary goal

License

This project is licensed under the GNU General Public License v3.0 or later (GPLv3+) - see the LICENSE file for details.

Acknowledgments

• Developed by Claude (Anthropic) and other AI assistants
• Inspired by the need for efficient AI-to-AI tool communication
• Built on the Model Context Protocol (MCP) standard