MCP server for PSR Cloud (local stdio)
Project description
PSR Cloud MCP Server
MCP server that exposes PSR Cloud HPC operations as AI tools, built on top of
pycloud (psr-cloud on PyPI).
Prerequisites
- Python 3.10+
- A Personal Access Token from PSR Cloud:
- Go to
sso.psr-inc.com/profile - Log in with your corporate PSR Google account
- Generate a token under PSR Cloud — Personal Access Tokens
- Save the token value (shown only once)
- Go to
Installation
From PyPI
pip install psr-cloud-mcp
From the Git repository
pip install git+https://github.com/your-org/psr-cloud-mcp.git
Local development clone
git clone https://github.com/your-org/psr-cloud-mcp.git
cd psr-cloud-mcp/local
pip install -e .
Configuring Claude Code
Add to ~/.claude/settings.json (or settings.local.json for local-only):
{
"mcpServers": {
"psr-cloud": {
"command": "psr-cloud-mcp",
"env": {
"PSR_CLOUD_EMAIL": "yourname@psr-inc.com",
"PSR_CLOUD_ACCESS_TOKEN": "<your-token>"
}
}
}
}
If you prefer not to store the token in the config file, omit the env block and
set the variables in your shell profile instead.
Configuring Claude Desktop
Open the Claude Desktop config file:
- Windows:
%APPDATA%\Claude\claude_desktop_config.json - macOS:
~/Library/Application Support/Claude/claude_desktop_config.json
Add the psr-cloud entry under mcpServers:
{
"mcpServers": {
"psr-cloud": {
"command": "psr-cloud-mcp",
"env": {
"PSR_CLOUD_EMAIL": "yourname@psr-inc.com",
"PSR_CLOUD_ACCESS_TOKEN": "<your-token>"
}
}
}
}
If psr-cloud-mcp is not on the system PATH (common on Windows), use the full
path to the executable:
{
"mcpServers": {
"psr-cloud": {
"command": "C:\\Users\\<user>\\AppData\\Local\\Programs\\Python\\Python3xx\\Scripts\\psr-cloud-mcp.exe",
"env": {
"PSR_CLOUD_EMAIL": "yourname@psr-inc.com",
"PSR_CLOUD_ACCESS_TOKEN": "<your-token>"
}
}
}
}
To find the exact executable path on Windows, run:
where.exe psr-cloud-mcp
After saving the file, restart Claude Desktop. The PSR Cloud tools will appear in the tools panel (hammer icon).
Available tools
| Tool | Description |
|---|---|
list_cases |
List cases from the last N days |
get_cases |
Get details for specific case IDs |
get_case_status |
Poll execution status of a case |
get_case_log |
Retrieve execution log text |
list_download_files |
List result files available for a case |
run_case |
Submit a new model run |
cancel_case |
Cancel a running or queued case |
download_results |
Download result files to a local path |
get_programs |
List available programs (SDDP, OPTGEN, …) |
get_program_versions |
List versions for a program |
get_execution_types |
List execution types for a program + version |
get_memory_per_process_ratios |
List valid memory ratio strings |
Typical AI workflow
get_programs()
→ get_program_versions("SDDP")
→ get_execution_types("SDDP", "18.0")
→ run_case(name="my-run", data_path="/path/to/data", program="SDDP", ...)
→ get_case_status(case_id) # poll until SUCCESS
→ list_download_files(case_id)
→ download_results(case_id, output_path="/path/to/results")
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