Environment¶
Mainly providing MCP servers in an independent environment to support high concurrency applications of MCP servers.
Add MCP Servers¶
If you only use the built-in MCP servers, you only need to deploy them.
If there is a new MCP server and you want to use it independently, you can use the same directory structure as gaia-mcp-server, and refer to the code structure and implementation of hello_world.
your_mcp_server/
.dockerignore
Dockfile
mcp_servers/
.env
.gitignore
mcp_config.py
build_mcp_tool_schema.py
init_env.sh
your_tool/
src/
.python-version
pyproject.toml
The mcp_config variable in mcp_config.py is a standard MCP configuration structure.
Before deployment, it is necessary to run build_mcp_tool_schema.py to generate mcp_tool_schema.json.
.env is the environment configuration file for MCP servers.
Depolyment¶
Local Docker Deployment¶
Prerequisites¶
Ensure Docker and Docker Compose are properly installed and operational:
# Verify Docker installation
docker --version
docker compose --version
# Verify Docker daemon is running
docker ps
docker compose ps
Step 1: Launch VirtualPC MCP Server
Monitor the terminal output for any errors during startup.
Step 2: Connect to VirtualPC MCP Server
Use the following configuration to connect to the VirtualPC MCP Server:
{
"virtualpc-mcp-server": {
"type": "streamable-http",
"url": "http://localhost:8000/mcp",
"headers": {
"Authorization": "Bearer your token",
"MCP_SERVERS": "readweb-server,browser-server"
},
"timeout": 6000,
"sse_read_timeout": 6000,
"client_session_timeout_seconds": 6000
}
}
Note: The Bearer token is your own. The MCP_SERVERS header specifies the MCP server
scope for your current connection, which should be a subset of server names defined in
mcp_servers/mcp_config.py.
Kubernetes Cluster Deployment¶
For production deployments and RL training scenarios, Kubernetes cluster deployment is recommended. Detailed instructions will be provided in future updates.