taac-platform-api-inspection

TAAC Platform API Inspection

Use when a user asks to inspect TAAC/Taiji training pages or analyze metrics. Get structured data from the authenticated browser session. Never estimate values from screenshots.

Core Principles

• Always use page.evaluate() + fetch({ credentials: "include" }) for API calls. page.context().request.get() fails with Storage.getCookies: Method not found in the MCP browser environment.
• Always summarize inside page.evaluate(). Raw tf_events JSON is 50–200 KB and will be truncated into ephemeral resource files. Return only compact summaries — one object per metric.
• Response interception + page.reload() is the fastest discovery method. One reload captures both get_ckpt and tf_events endpoints.

Two-Step Pipeline

Step 1: Navigate and discover endpoints

Navigate to the checkpoint URL, then intercept XHR responses during reload:

// run_playwright_code — intercept + reload
const captured = [];
const keywords = ["api", "training", "ckpt", "metric", "scalar", "plot", "instance", "tf_events"];
const handler = async (response) => {
  const url = response.url();
  if (!keywords.some(kw => url.toLowerCase().includes(kw))) return;
  const ct = response.headers()["content-type"] || "";
  const item = { url, status: response.status(), contentType: ct };
  if (ct.includes("json")) {
    try { item.preview = (await response.text()).slice(0, 4000); } catch (e) { item.error = String(e); }
  }
  captured.push(item);
};
page.on("response", handler);
await page.reload({ waitUntil: "networkidle" });
page.off("response", handler);
return captured;

This surfaces:

• .../external/<instance_id>/get_ckpt — checkpoint list, sizes, publish status
• .../external/<instance_id>/tf_events — all scalar metrics

Extract <instance_id> from the URL: it's the last path segment (95d1b0469e0fba1e019e1811651b16d5 in a typical checkpoint URL).

If the browser redirects to https://algo.qq.com/?type=login, stop and ask the user to log in first.

Step 2: Fetch and summarize metrics in-browser

// run_playwright_code — fetch + summarize
return page.evaluate(async () => {
  const url = 'https://taiji.algo.qq.com/taskmanagement/api/v1/instances/external/<INSTANCE_ID>/tf_events';
  const json = await (await fetch(url, { credentials: 'include' })).json();
  const groups = json.data?.data ?? {};
  const summaries = [];

  for (const [group, charts] of Object.entries(groups)) {
    if (!Array.isArray(charts)) continue;
    for (const chart of charts) {
      const dates = chart.date || [];
      const titles = chart.title || [];
      const values = chart.value || [];
      for (let i = 0; i < titles.length; i++) {
        const name = titles[i];
        const vals = values[i] || [];
        const points = dates
          .map((step, j) => ({ step: +step, value: +vals[j] }))
          .filter(p => Number.isFinite(p.step) && Number.isFinite(p.value));
        if (!points.length) continue;

        let min = points[0], max = points[0];
        for (const p of points) {
          if (p.value < min.value) min = p;
          if (p.value > max.value) max = p;
        }
        const n = points.length;
        const earlyN = Math.max(1, Math.floor(n * 0.2));
        const lateN = Math.max(1, Math.floor(n * 0.2));
        const earlyAvg = points.slice(0, earlyN).reduce((s, p) => s + p.value, 0) / earlyN;
        const lateAvg = points.slice(n - lateN).reduce((s, p) => s + p.value, 0) / lateN;
        const trend = lateAvg > earlyAvg * 1.005 ? '↑' : lateAvg < earlyAvg * 0.995 ? '↓' : '→';

        summaries.push({
          group, name, count: n,
          first: [points[0].step, +points[0].value.toFixed(6)],
          last:  [points[n-1].step, +points[n-1].value.toFixed(6)],
          min:   [min.step, +min.value.toFixed(6)],
          max:   [max.step, +max.value.toFixed(6)],
          trend
        });
      }
    }
  }
  return summaries;
});

Replace <INSTANCE_ID> with the value from Step 1. The result is a compact array, e.g. {group:"AUC", name:"AUC/valid", first:[500,0.7929], last:[5000,0.8296], min:[500,0.7929], max:[2500,0.8346], trend:"↑"}.

Analysis Framework

When reporting results, structure the analysis around these signals:

Primary signals (always check)

Symbiosis-specific signals (when present)

Probe metrics (when present)

Compare Probe/auc, Probe/logloss, Probe/auc_retention against AUC/valid and LogLoss/valid. Probe degradation while full metrics improve indicates fragile representations.

Pitfalls

• Don't return raw JSON. If you see "Large tool result written to file", you've already failed — re-run with in-browser summarization.
• json.data?.data not json.data. The tf_events endpoint nests metric groups one level deeper than expected.
• Guard with Array.isArray(charts). Not all entries under json.data?.data are arrays.
• Console noise is normal. WeChat login probes (localhost.weixin.qq.com, ERR_CONNECTION_CLOSED), preload warnings, and telemetry errors are page noise, not training failures.
• Don't click Publish/Delete/Cancel/Submit unless the user explicitly asks.

Safety

• Never expose cookies, tokens, or authorization headers.
• Prefer browser-context requests over terminal curl.
• Don't commit downloaded platform payloads or screenshots.