
AutogenStudio-Skills
Repo des compétences pour Autogen Studio à l'aide du Protocole de contexte du modèle (MCP)
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AutoGen Studio MCP Tools
What is MCP?
Model Context Protocol (MCP) is like a USB for AI tools - it provides a standardized way for AI agents to discover and use different capabilities. Think of it as plugging in new abilities for your AI agents! With our flexible dictionary-based implementation, agents can easily discover and use tools without needing to know their implementation details.
Available MCP Servers
The MCP ecosystem is growing rapidly with many powerful servers available:
- Brave Search - Web and local search capabilities
- Filesystem - Complete file and directory operations
- Playwright - Browser automation and web scraping
- MongoDB - Direct database interactions
- Sequential Thinking - Structured reasoning and planning
- FLUX - Image generation and manipulation
- Selenium - Web automation and testing
- SQLite - Local database operations
- Vector Store - Embedding and similarity search
- And many more!
Check out the MCP Servers Repository for the latest list of available servers. The ecosystem is rapidly growing with new capabilities being added regularly!
Quick Start
- Install Requirements:
# Create and activate virtual environment
python -m venv .env
source .env/bin/activate # On Windows: .env\Scripts\activate
# Install required packages
pip install mcp
- Configure MCP:
# Copy the example config
cp mcp_config.example.json mcp_config.json
# Edit mcp_config.json with your settings
{
"mcpServers": {
"brave-search": {
"enabled": true,
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-brave-search"],
"env": {
"BRAVE_API_KEY": "your-api-key-here"
}
},
"filesystem": {
"enabled": true,
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/your/working/directory"
]
}
}
}
- Start AutoGen Studio:
autogenstudio ui --port 8080
Dynamic Tool Usage
Our implementation uses a flexible dictionary-based approach, making it easy to discover and use tools:
# 1. List Available Servers
servers = await mcp(tool='list_available_servers')
# 2. Discover Server Tools
tools = await mcp(
server='brave-search',
tool='tool_details'
)
# 3. Use Tools with Dictionary Arguments
# Web Search Example
result = await mcp(
server='brave-search',
tool='brave_web_search',
arguments={
'query': 'Latest AI developments',
'count': 5
}
)
# File Operations Example
result = await mcp(
server='filesystem',
tool='read_file',
arguments={
'path': '/path/to/your/file'
}
)
Example Templates
We provide ready-to-use templates to help you get started:
-
Gallery Examples (
/gallerys
):- Pre-configured tool setups
- Example implementations
- Best practices demonstrations
-
Team Templates (
/teams
):- Complete agent workflows
- Tool integration examples
- Task-specific configurations
To use a template:
- Copy the desired template from
/gallerys
or/teams
- Customize the JSON configuration for your needs
- Load it in AutoGen Studio
Available Tools
1. Brave Search
- Web search capabilities
- Local business search
- Configurable result limits
- Fresh content filtering
2. File Operations
- Read/Write files
- Directory operations
- File searches
- Metadata operations
3. More Coming Soon
The MCP ecosystem is constantly growing. Add new servers to your config to expand capabilities!
Configuration Locations
The MCP client checks these locations for config files:
- Current directory:
./mcp_config.json
- User config:
~/.config/autogen/mcp_config.json
- Environment:
$MCP_CONFIG_PATH
Development
Testing
Run the test suite:
pytest tests/test_mcp_client.py -v
pytest tests/test_mcp_json.py -v
Workspace
The /mcp_workspace
directory is provided for testing but ignored by git. Your tests will create this automatically.
Requirements
- Python 3.12+
- Node.js and npx
- MCP Python SDK
- AutoGen Studio
License
MIT License - See LICENSE file for details
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Reviews

user_1z9MMNPg
I've been using Autogenstudio-Skills by madtank and it's simply awesome! The functionality and ease of use have significantly boosted my productivity. The repository on GitHub is well-documented and the support is top-notch. Highly recommend it for anyone looking to automate workflows effectively!