A Python-based MCP (Model Context Protocol) server that analyzes MicroShift test failures from Google Sheets, providing specialized tools for
Mcp test scenarios server mcp server connects MCP Test Scenarios Server to AI assistants that speak the Model Context Protocol. A Python-based MCP (Model Context Protocol) server that analyzes MicroShift test failures from Google Sheets, providing specialized tools for correlating test failures with MicroShift versions.
A Python-based MCP (Model Context Protocol) server that analyzes MicroShift test failures from Google Sheets, providing specialized tools for correlating test failures with MicroShift versions.
Setup follows the standard MCP pattern: clone or install the server, then register it in your client's configuration file and restart the client. The configuration snippets on this page cover Claude Desktop, Claude Code and Cursor.
Before the server will start you need to supply 2 environment variables: GOOGLE_PRIVATE_KEY, SPREADSHEET_ID. Keep credentials in your client's env block or a secrets manager rather than committing them.
Developer-tool servers are usually the first ones people connect, because they turn "help me with this code" into an assistant that can actually read the repo and act on it. MCP Test Scenarios Server sits in that group. Worth comparing against the other developer tools servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
{
"mcpServers": {
"microshift-test-analyzer": {
"command": "python",
"args": ["/absolute/path/to/mcp-test-scenarios-server/server.py"],
"cwd": "/absolute/path/to/mcp-test-scenarios-server"
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| GOOGLE_PRIVATE_KEY | Credential the server authenticates with. | Yes |
| SPREADSHEET_ID | Configuration value read at startup. | Optional |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.