dpdispatcher-mcp-server

# Dispatcher MCP Server An MCP (Model Context Protocol) server that acts as a wrapper around the `dpdispatcher` library. It allows language models or other MCP clients to submit and manage computational jobs on local machines or HPC clusters supported by `dpdispatcher`. ## Features * Exposes `dpdispatcher` functionality via standard MCP tools. * **`submit_job`**: Submits a new computation job. * **`query_status`**: Checks the status of a submitted job. * **`cancel_job`**: Attempts to cancel a running or queued job. * **`fetch_result`**: Retrieves the paths of result files for a completed job. * Includes MCP Resources and Prompts to guide interactive job configuration. * Supports stdio transport for local integration (e.g., with Cline). ## Setup 1. **Prerequisites**: * Python 3.x * `dpdispatcher` library installed (`pip install dpdispatcher`) * `mcp` library installed (`pip install mcp`) * `anyio` library installed (`pip install anyio`) 2. **Clone/Place Files**: Ensure `fast_server.py` and `job_manager.py` (and `__init__.py`) are within a directory (e.g., `dispatcher_mcp_server`). 3. **Configure `dpdispatcher`**: If submitting to remote HPCs or Bohrium, ensure `dpdispatcher` itself is correctly configured (e.g., SSH keys, Bohrium credentials). ## Running the Server Navigate to the parent directory containing `dispatcher_mcp_server` and run: ```bash python dispatcher_mcp_server/fast_server.py ``` The server will start and listen via stdio. ## Integration with MCP Clients (e.g., Cline) Add the following configuration to your client's MCP settings (e.g., `mcp_settings.json` for Cline), adjusting paths as necessary: ```json { "mcpServers": { "dispatcher-mcp-server": { "command": "python", "args": [ "dispatcher_mcp_server/fast_server.py" ], "cwd": "/path/to/parent/directory/containing/dispatcher_mcp_server", "env": { "PYTHONPATH": "/path/to/parent/directory/containing/dispatcher_mcp_server" }, "disabled": false } } } ``` Restart the client to load the server. ## Usage Interact with the server using an MCP client. You can directly call tools like `submit_job` by providing the necessary arguments (machine config, resources config, task details), or use the `configure_job` prompt to guide an LLM through an interactive configuration process. Helper resources like `dpd://examples/machine/{type}` are available for context.