mcp_arena

mcp_arena is a production-ready Python library for building MCP (Model Context Protocol) servers with intelligent agent orchestration and domain-specific presets.

✨ Features

• 🚀 Ready-to-use MCP servers for popular platforms (GitHub, Slack, Notion, AWS, etc.)
• 🤖 Intelligent agents with reflection, planning, and routing capabilities
• 🔧 Zero-configuration setup for common use cases
• 🏗️ Extensible architecture built on SOLID principles
• 📦 Modular design - use only what you need

🚀 Quick Start

Installation

# Core library
pip install mcp-arena

# With specific presets
pip install mcp-arena[github,slack,notion]

# All presets
pip install mcp-arena[all]

Basic Usage

from mcp_arena.presents.github import GithubMCPServer

# Zero-config GitHub MCP server
mcp_server = GithubMCPServer(token="your_github_token")
mcp_server.run()

Using Tools Directly

from mcp_arena.tools.github import GithubTools
from mcp_arena.presents.github import GithubMCPServer

# Create GitHub MCP server first
mcp_server = GithubMCPServer(token="your_token")

# Create tools wrapper
tool = GithubTools(server=mcp_server)
tools = tool.get_list_of_tools()

@mcp_server.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b


# Add a dynamic greeting resource
@mcp_servevr.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Get a personalized greeting"""
    return f"Hello, {name}!"

@mcp_server.prompt()
def greet_user(name: str, style: str = "friendly") -> str:
    """Generate a greeting prompt"""
    styles = {
        "friendly": "Please write a warm, friendly greeting",
        "formal": "Please write a formal, professional greeting",
        "casual": "Please write a casual, relaxed greeting",
    }

    return f"{styles.get(style, styles['friendly'])} for someone named {name}."

Advance Documentation

from mcp.server.fastmcp import Icon
from mcp_arena.presents.github import GithubMCPServer

# Create an icon from a file path or URL
icon = Icon(
    src="icon.png",
    mimeType="image/png",
    sizes="64x64"
)

# Add icons to server
mcp = GithubMCPServer(
    "My Server",
    website_url="https://example.com",
    token="*******",
    icons=[icon]
)

# Add icons to tools, resources, and prompts
@mcp.tool(icons=[icon])
def my_tool():
    """Tool with an icon."""
    return "result"

@mcp.resource("demo://resource", icons=[icon])
def my_resource():
    """Resource with an icon."""
    return "content"

With Agent Orchestration

from mcp_arena.presents.github import GithubMCPServer
from mcp_arena.agent.react_agent import ReactAgent

# Create MCP server
mcp_server = GithubMCPServer(token="your_token")

# Create agent separately
agent = ReactAgent(llm=None, memory_type="conversation")

# Run the server
mcp_server.run()

LangChain Integration

Using MCP Arena Wrapper

from mcp_arena.wrapper.langchain_wrapper import MCPLangChainWrapper
from mcp_arena.presents.github import GithubMCPServer

# Create MCP server
github_server = GithubMCPServer(token="your_token")

# Wrap with LangChain
wrapper = MCPLangChainWrapper(
    servers={"github": github_server},
    auto_start=True
)

# Connect and create agent
await wrapper.connect()
agent = wrapper.create_agent(
    llm="gpt-4-turbo",
    system_prompt="You are a GitHub assistant"
)

Direct langchain_mcp_adapters Usage

from langchain_mcp_adapters.client import MultiServerMCPClient  
from langchain.agents import create_agent
from mcp_arena.presents.github import GithubMCPServer

# Start GitHub MCP server in background
github_server = GithubMCPServer(token="your_token", transport="stdio")
github_server.run()

# Create client with multiple servers
client = MultiServerMCPClient(  
    {
        "github": {
            "transport": "stdio",
            "command": "python",
            "args": ["/path/to/github_server_script.py"],
        },
        "math": {
            "transport": "http",
            "url": "http://localhost:8001/mcp",
        }
    }
)

tools = await client.get_tools()  
agent = create_agent(
    "claude-sonnet-4-5-20250929",
    tools  
)

# Use the agent
github_response = await agent.ainvoke(
    {"messages": [{"role": "user", "content": "List my GitHub repositories"}]}
)
math_response = await agent.ainvoke(
    {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
)

📚 Available Presets

Browser & Automation

• Browser - Browser automation with Playwright (navigate, screenshot, forms, extract data)
• Screen Capture - Take screenshots and screen recordings with PyAutoGUI

Video & Media

• Video - Video editing (trim, merge, effects, format conversion) with FFmpeg
• PDF - PDF processing (extract text/images, merge, split, watermark, encrypt)
• QR Code - Generate and decode QR codes

Data & Files

• Spreadsheet - Excel/CSV read/write with pandas and openpyxl
• Web Scraping - Extract data from websites with requests and BeautifulSoup

Communication

• Slack - Channels, messages, workflows
• WhatsApp - Messaging via Twilio API
• Gmail - Email management and sending
• Outlook - Microsoft 365 email and calendar
• Discord - Servers and channels
• Teams - Microsoft Teams integration
• Notification - Multi-platform notifications (Email, Slack, webhooks)

Development Platforms

• GitHub - Repositories, issues, PRs, workflows
• GitLab - Projects, CI/CD, issues
• Bitbucket - Repositories and pipelines

Productivity

• Notion - Databases, pages, blocks
• Confluence - Spaces and pages
• Jira - Projects, issues, workflows

Cloud Services

• AWS S3 - Storage operations
• Azure Blob - Azure storage
• Google Cloud Storage - GCP storage

System Operations

• Local Operations - File system and system ops
• Docker - Container management
• Kubernetes - Cluster operations

🤖 Agent Types

Reflection Agent

Self-improving agent that refines responses through iterative refinement.

from mcp_arena.agent.reflection_agent import ReflectionAgent

agent = ReflectionAgent(
    llm=None,
    memory_type="conversation"
)

ReAct Agent

Systematic reasoning and acting cycle for complex problem-solving.

from mcp_arena.agent.react_agent import ReactAgent

agent = ReactAgent(
    llm=None,
    memory_type="conversation"
)

Planning Agent

Goal decomposition and step-by-step execution for complex tasks.

from mcp_arena.agent.planning_agent import PlanningAgent

agent = PlanningAgent(
    llm=None,
    memory_type="conversation"
)

Router Agent

Dynamic agent selection based on task requirements.

from mcp_arena.agent.router import AgentRouter

router = AgentRouter()

# Add routing rules
router.add_route(
    condition=lambda input_text: "github" in input_text.lower(),
    agent_type="react",
    config={"llm": your_llm}
)

router.add_route(
    condition=lambda input_text: "reflect" in input_text.lower(),
    agent_type="reflection",
    config={"llm": your_llm}
)

🔧 Custom Tools

Extend any preset with custom tools:

from mcp_arena.presents.github import GithubMCPServer
from mcp_arena.tools.base import tool

@tool(description="Custom repository analyzer")
def analyze_repo(repo: str) -> str:
    return f"Analysis for {repo}"

server = GithubMCPServer(
    token="your_token",
    extra_tools=[analyze_repo]
)

🤖 LangChain Integration

Integrate mcp_arena MCP servers with LangChain agents for powerful multi-service automation:

from langchain_openai import ChatOpenAI
from mcp_arena.wrapper.langchain_wrapper import MCPLangChainWrapper
from mcp_arena.presents.browser import BrowserMCPServer

# Initialize LLM
llm = ChatOpenAI(model="gpt-4-turbo")

# Create wrapper with browser server
wrapper = MCPLangChainWrapper(
    servers={"browser": BrowserMCPServer(headless=True)},
    auto_start=True
)

# Connect and create agent
await wrapper.connect()
agent = wrapper.create_agent(
    llm=llm,
    system_prompt="You are a helpful browser automation assistant"
)

# Use the agent
response = await wrapper.invoke_agent(
    agent,
    "Go to example.com and tell me the page title"
)

Multi-Server Agent Example

from langchain_openai import ChatOpenAI
from mcp_arena.wrapper.langchain_wrapper import MCPLangChainWrapper
from mcp_arena.presents.browser import BrowserMCPServer
from mcp_arena.presents.pdf import PDFMCPServer
from mcp_arena.presents.web_scraping import WebScrapingMCPServer

# Initialize LLM
llm = ChatOpenAI(model="gpt-4-turbo")

# Create wrapper with multiple servers
wrapper = MCPLangChainWrapper(
    servers={
        "browser": BrowserMCPServer(headless=True),
        "pdf": PDFMCPServer(),
        "web": WebScrapingMCPServer()
    },
    auto_start=True
)

# Connect and create agent with all tools
await wrapper.connect()
agent = wrapper.create_agent(
    llm=llm,
    system_prompt="""You are a powerful research assistant with access to:
    - Browser automation (navigate websites, take screenshots)
    - PDF processing (extract text, merge, split)
    - Web scraping (extract data from websites)
    """
)

# Use the agent
response = await wrapper.invoke_agent(
    agent,
    "Research climate change: find a Wikipedia article, take a screenshot, and extract key facts to a PDF"
)

Installation:

pip install langchain-openai langchain-mcp-adapters
pip install "mcp-arena[browser,video,pdf,webscraping]"

📖 Full Documentation 📖 Agent Examples

🏗️ Custom MCP Server

Build from scratch for full control:

from mcp_arena.mcp.server import BaseMCPServer
from mcp_arena.tools.base import tool

@tool(description="Search internal docs")
def search_docs(query: str) -> str:
    return f"Results for {query}"

class CustomMCPServer(BaseMCPServer):
    def _register_tools(self):
        self.add_tool(search_docs)

server = CustomMCPServer(
    name="custom-server",
    description="Custom MCP server"
)
server.run()

📖 Documentation

• Installation Guide - Detailed installation instructions for all presets and communication services
• MCP Servers Guide - Comprehensive guide to all 17 available MCP servers
• Agent Guide - Using and configuring intelligent agents
• Tools Guide - Tool development and integration
• LangChain Integration - Integrate MCP servers with LangChain agents
• Quick Start - Get started in minutes
• Tutorial - Step-by-step tutorial

Architecture

MCP Client
   │
   ▼
┌─────────────────┐
│   MCP Server    │  ← Core Layer
│ - Protocol      │
│ - Auth          │
│ - Tool Registry │
└─────────────────┘
   │
   ▼
┌─────────────────┐
│  Agent System   │  ← Intelligence Layer
│ - Reflection    │
│ - ReAct         │
│ - Planning      │
│ - Router        │
└─────────────────┘
   │
   ▼
┌─────────────────┐
│ Tool Ecosystem  │  ← Execution Layer
│ - Presets       │
│ - Custom Tools  │
│ - Orchestration │
└─────────────────┘

Installation Options

# Core only
pip install mcp-arena[core]

# Browser automation
pip install mcp-arena[browser]

# Video editing
pip install mcp-arena[video]

# PDF processing
pip install mcp-arena[pdf]

# QR code generation
pip install mcp-arena[qrcode]

# Spreadsheet operations
pip install mcp-arena[spreadsheet]

# Web scraping
pip install mcp-arena[webscraping]

# Screen capture
pip install mcp-arena[screencapture]

# Cloud storage (AWS S3, GCS, Azure)
pip install mcp-arena[cloudstorage]

# Notifications (Email, Slack, webhooks)
pip install mcp-arena[notification]

# Development platforms
pip install mcp-arena[github,gitlab,bitbucket]

# Data & storage
pip install mcp-arena[postgres,mongodb,redis,vectordb]

# Communication
pip install mcp-arena[slack,whatsapp,gmail,outlook]

# All communication services
pip install mcp-arena[communication]

# Productivity
pip install mcp-arena[notion,confluence,jira]

# Cloud services
pip install mcp-arena[aws,docker,kubernetes]

# System operations
pip install mcp-arena[local_operation]

# Agent framework
pip install mcp-arena[agents]

# All presets
pip install mcp-arena[all]

# Complete with dev tools
pip install mcp-arena[complete]

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/SatyamSingh8306/mcp_arena.git
cd mcp_arena

# Install in development mode
pip install -e .[dev]

# Run tests
pytest

# Run linting
black .
isort .
mypy .

Priority Areas

• New preset implementations
• Agent pattern improvements
• Documentation and examples
• Bug fixes and performance

📋 Requirements

• Python 3.12+
• MCP client compatible with Model Context Protocol v1.0+

📄 License

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

🔗 Links

• Documentation - Complete documentation library
• Installation Guide - Installation instructions
• MCP Servers Guide - Server documentation
• LangChain Integration - LangChain integration guide
• Repository
• Issues
• PyPI

🚧 Status

Version: 0.2.1 (Production-ready)

✅ Stable Features:

• MCP server base classes
• 17 production-ready presets
• 4 agent types
• Tool registration system
• SOLID architecture
• Communication services (Gmail, Outlook, Slack, WhatsApp)

🔄 Evolving APIs:

• Agent interfaces may enhance based on feedback
• New preset additions
• Performance optimizations

📈 Production Ready:

• Comprehensive documentation
• Active development
• Community support