MCP server for key driver and feature importance analysis based on rule mining
MCP server for key driver and feature importance analysis based on rule mining. Exposed over MCP by the key mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
MCP server for key driver and feature importance analysis. Load any CSV dataset and ask what drives an outcome — survival, credit default, diagnosis, income — and get a ranked breakdown with sub-driver analysis showing not just which factors matter, but how they combine to amplify or completely reverse each other.
The server ships on PyPI as key-drivers-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Everything the assistant can do here goes through one of these:
load_dataset — Load a CSV file into session memorylist_datasets — List all loaded datasetsfind_drivers — Find key drivers of a target outcomeexplain_segment — Driver analysis conditioned on a segment variable (CLARA)araxai >= 0.3.0 - mcp[cli] >= 1.0.0This sits in the knowledge and memory group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Key's toolset — load_dataset, list_datasets, find_drivers and 1 more — is a fair guide to whether it matches your workflow.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| load_dataset | Load a CSV file into session memory |
| list_datasets | List all loaded datasets |
| find_drivers | Find key drivers of a target outcome |
| explain_segment | Driver analysis conditioned on a segment variable (CLARA) |
{
"mcpServers": {
"key-drivers": {
"type": "stdio",
"command": "uvx",
"args": ["key-drivers-mcp"]
}
}
}Configuration as documented by the project. Restart the client after saving.
araxai >= 0.3.0 - mcp[cli] >= 1.0.0A 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.