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ml-feature-extract

station__ml-pipeline__ml-feature-extract external (needs EXECUTION_BACKEND_URL) ml-pipeline non-pv Constructa Configa

Extract 12-element PV feature vector from FAERS 2x2 contingency table: PRR, ROR, IC, EBGM, log(cases), HCP ratio, consumer ratio, serious ratio, death ratio, hospitalization ratio, median time-to-onset, reporting velocity

Taxonomy

Linnaean classification joined from the algovigilance taxonomy index via the parent config's rank.

Rank	Value
domain	`Substrata`
kingdom	`Constructa`
phylum	`Configa`
class	`station-config`
order	`ml`
family	`mcp-tool-config`

Characteristics:

substrate: config
domain: pv
lifecycle: continuous
authority: read
compounding: producer
io: agent-request → tool-response

Input schema

a integerrequired — Contingency table cell: cases with both drug and event.
b integerrequired — Contingency table cell: cases with drug but not event.
c integerrequired — Contingency table cell: cases with event but not drug.
consumer_reports integer
d integerrequired — Contingency table cell: cases with neither.
death_count integer
drug stringrequired — Drug name (generic).
event stringrequired — Event name (MedDRA preferred term).
hcp_reports integer
hospitalization_count integer
median_tto_days number
serious_count integer
velocity number

Example call

POST /api/mcp
Content-Type: application/json

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "station__ml-pipeline__ml-feature-extract",
    "arguments": {
      "a": 0,
      "b": 0,
      "c": 0,
      "d": 0,
      "drug": "",
      "event": ""
    }
  }
}

How to invoke from a client

From any MCP-aware client, add https://algovigilance.com/api/mcp as an MCP server, then call this tool by name. From a raw HTTP client, send the JSON-RPC body above to /api/mcp.

Agent-friendly formats

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