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