Agent Control via MCP
devsy mcp serve runs a Model Context Protocol server over stdio. AI clients that support MCP can use it to list, create, and run commands in workspaces.
devsy mcp serveConfigure your MCP client to launch that command. The exact config format depends on the client. A typical shape:
{
"mcpServers": {
"devsy": {
"command": "devsy",
"args": ["mcp", "serve"]
}
}
}Tools
| Tool | Purpose |
|---|---|
workspace_list | List workspaces with provider, IDE, and source. |
workspace_status | Get the saved workspace configuration. It does not report whether the workspace is running. |
workspace_create | Create and start a workspace. |
workspace_start | Start an existing workspace. |
workspace_stop | Stop a running workspace. |
workspace_delete | Delete a workspace. Accepts force=true. |
workspace_exec | Run a one-shot command in a running workspace. |
provider_list | List configured providers. |
provider_add | Add a provider from a registry name, GitHub URL, or local path. |
provider_delete | Remove a provider. |
provider_use | Set the default provider. |
To check whether a workspace is running, use devsy workspace status <name> --result-format json.
workspace_exec is limited by flags on devsy mcp serve: --exec-timeout-default (5m), --exec-timeout-max (30m), and --exec-output-cap (100 KiB per stream). --mcp-max-concurrent-ops (8) limits concurrent exec, create, and start calls.
Agent skill
Devsy also publishes an agent skill that teaches an agent how to pick workspaces and providers, run commands, and recover from timeouts. It is separate from the MCP server: installing it does not install Devsy or configure MCP. It needs Node.js for npx.
npx skills add devsy-org/devsy --skill devsyAdd -g to install it for all projects, or --agent to target one agent.
Security
The MCP server has the same authority as your local Devsy CLI. A connected client can create and delete workspaces, run any command in them, and add or remove providers. Only use it with clients you trust.