What is Dpdispatcher Mcp Server

dpdispatcher-mcp-server is an MCP (Model Context Protocol) server that serves as a wrapper around the dpdispatcher library, enabling language models or other MCP clients to submit and manage computational jobs on local machines or HPC clusters supported by dpdispatcher.

Use cases

Use cases for dpdispatcher-mcp-server include submitting and managing jobs for language model training, running data processing tasks on HPC clusters, and integrating with other MCP clients for seamless computational workflows.

How to use

To use dpdispatcher-mcp-server, ensure you have Python 3.x and the required libraries (dpdispatcher, mcp, anyio) installed. Clone the necessary files into a directory, configure dpdispatcher if needed, and run the server using the command ‘python dispatcher_mcp_server/fast_server.py’.

Key features

Key features include exposing dpdispatcher functionality through standard MCP tools, job submission, status querying, job cancellation, result fetching, interactive job configuration guidance, and support for stdio transport for local integration.

Where to use

dpdispatcher-mcp-server can be used in fields that require computational job management, such as machine learning, data analysis, and high-performance computing (HPC) environments.

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:

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:

{
  "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.