trainClassifier.ts•3.83 kB
import { z } from "zod";
import type { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { runTrainingJob, type TrainingTaskStatus } from "../services/trainingService.js";
import { SshExecuteOutputSchema } from "./sshExecute.js";
import { createProgressReporter } from "../utils/progress.js";
const TrainClassifierInputSchema = z.object({
profile: z.string().describe("Credential profile for SSH access"),
subclasses: z.array(z.string()).nonempty().describe("List of subclasses to train"),
datasetPath: z.string().describe("Remote dataset path"),
commandTemplate: z
.string()
.optional()
.describe("Command template to run on the remote host; overrides configuration if provided"),
timeoutMs: z
.number()
.int()
.positive()
.optional()
.describe("Optional timeout per subclass execution in milliseconds"),
dryRun: z.boolean().optional().describe("If true, render commands without executing them")
});
type TrainClassifierInput = z.infer<typeof TrainClassifierInputSchema>;
const TrainingStatusValues = ["pending", "running", "succeeded", "failed", "cancelled"] as const satisfies TrainingTaskStatus[];
const TrainingStatusSchema = z.enum(TrainingStatusValues);
const TrainingTaskLogSchema = z.object({
level: z.enum(["info", "warn", "error"]),
message: z.string(),
at: z.string(),
context: z.record(z.string(), z.unknown()).optional()
});
const TrainingTaskReportSchema = z.object({
subclass: z.string(),
command: z.string(),
dryRun: z.boolean(),
status: TrainingStatusSchema,
startedAt: z.string().optional(),
completedAt: z.string().optional(),
result: SshExecuteOutputSchema.optional(),
error: z.string().optional(),
logs: z.array(TrainingTaskLogSchema)
});
const TrainingJobReportSchema = z.object({
profile: z.string(),
datasetPath: z.string(),
commandTemplate: z.string(),
status: TrainingStatusSchema,
startedAt: z.string(),
completedAt: z.string().optional(),
tasks: z.array(TrainingTaskReportSchema)
});
const TrainingJobOutputShape = {
job: TrainingJobReportSchema
};
export function registerTrainingTool(server: McpServer): void {
server.registerTool(
"trainClassifier",
{
description: "Run classifier training commands on a remote host via SSH",
inputSchema: TrainClassifierInputSchema.shape,
outputSchema: TrainingJobOutputShape
},
async (args, extra) => {
const input = TrainClassifierInputSchema.parse(args);
const total = input.subclasses.length;
const progress = createProgressReporter(extra, "trainClassifier");
progress?.({ progress: 0, total, message: "Preparing training job" });
const results = await runTrainingJob({
profile: input.profile,
subclasses: input.subclasses,
datasetPath: input.datasetPath,
commandTemplate: input.commandTemplate,
timeoutMs: input.timeoutMs,
dryRun: input.dryRun ?? false,
signal: extra.signal,
onProgress: (update) => {
progress?.({
progress: update.progress,
total,
message: update.message
});
}
});
progress?.({ progress: total, total, message: "Training job finished" });
const summaryLines = results.tasks.map((task) => {
const status = task.status.toUpperCase();
const duration = task.result?.durationMs !== undefined ? `${task.result.durationMs}ms` : "n/a";
return `- ${task.subclass}: ${status} (duration: ${duration})`;
});
return {
content: [
{
type: "text",
text: [`Training job status: ${results.status.toUpperCase()}`, ...summaryLines].join("\n")
}
],
structuredContent: {
job: results
}
};
}
);
}