Initial release: iai-mcp v0.1.0

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: XNLLLLH <XNLLLLH@users.noreply.github.com>

tests/test_trajectory_metrics.py

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+"""Tests for LEARN-07 trajectory metrics.
+
+every session_exit writes one trajectory_metric event per metric.
+Metrics:
+- M1: clarifying questions per session (decreasing)
+- M2: retrieval precision@5 (growing)
+- M3: tokens per session (decreasing)
+- M4: profile-vector variance (decreasing)
+- M5: curiosity question frequency (entropy dropping)
+- M6: context-repeat rate (> 90% by session ~20)
+"""
+from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from iai_mcp.events import query_events, write_event
+from iai_mcp.store import MemoryStore
+
+
+def test_metric_names_covers_m1_to_m6():
+    from iai_mcp.trajectory import METRIC_NAMES
+
+    assert set(METRIC_NAMES) == {"m1", "m2", "m3", "m4", "m5", "m6"}
+
+
+def test_record_session_metrics_writes_6_events(tmp_path):
+    from iai_mcp.trajectory import record_session_metrics
+
+    store = MemoryStore(path=tmp_path)
+    record_session_metrics(
+        store, session_id="s1",
+        metrics={"m1": 3.0, "m2": 0.7, "m3": 2000.0, "m4": 0.2, "m5": 1.0, "m6": 0.85},
+    )
+    events = query_events(store, kind="trajectory_metric")
+    assert len(events) == 6
+    metrics = {e["data"]["metric"] for e in events}
+    assert metrics == {"m1", "m2", "m3", "m4", "m5", "m6"}
+
+
+def test_record_session_metrics_ignores_bad_keys(tmp_path):
+    """m7 or bogus keys are ignored silently."""
+    from iai_mcp.trajectory import record_session_metrics
+
+    store = MemoryStore(path=tmp_path)
+    record_session_metrics(
+        store, session_id="s-bad",
+        metrics={"m1": 1.0, "m7_bogus": 42.0},
+    )
+    events = query_events(store, kind="trajectory_metric")
+    metrics = {e["data"]["metric"] for e in events}
+    assert "m7_bogus" not in metrics
+    assert "m1" in metrics
+
+
+def test_aggregate_trajectory_groups_by_metric(tmp_path):
+    from iai_mcp.trajectory import aggregate_trajectory, record_session_metrics
+
+    store = MemoryStore(path=tmp_path)
+    for i in range(3):
+        record_session_metrics(
+            store, session_id=f"s{i}",
+            metrics={"m1": float(i), "m2": float(i) * 0.1},
+        )
+    out = aggregate_trajectory(store)
+    assert "m1" in out
+    assert "m2" in out
+    assert len(out["m1"]) == 3
+    assert len(out["m2"]) == 3
+
+
+def test_aggregate_trajectory_since_filter(tmp_path):
+    from iai_mcp.trajectory import aggregate_trajectory, record_session_metrics
+
+    store = MemoryStore(path=tmp_path)
+    record_session_metrics(store, "s1", metrics={"m1": 1.0})
+    # Fetch with a since filter that excludes everything
+    future = datetime.now(timezone.utc) + timedelta(hours=1)
+    out = aggregate_trajectory(store, since=future)
+    assert sum(len(v) for v in out.values()) == 0
+
+
+def test_m1_clarifying_questions_signal(tmp_path):
+    """M1 = count of curiosity_question events in a session."""
+    from iai_mcp.trajectory import compute_m1_clarifying_questions_per_session
+
+    store = MemoryStore(path=tmp_path)
+    for _ in range(3):
+        write_event(
+            store, kind="curiosity_question",
+            data={}, session_id="s-m1",
+        )
+    val = compute_m1_clarifying_questions_per_session(store, "s-m1")
+    assert val == 3.0
+
+
+def test_m3_token_budget_signal(tmp_path):
+    """M3 = mean of session_start_tokens events for a session."""
+    from iai_mcp.trajectory import compute_m3_token_budget
+
+    store = MemoryStore(path=tmp_path)
+    for toks in (1000, 2000, 3000):
+        write_event(
+            store, kind="session_start_tokens",
+            data={"tokens": toks}, session_id="s-m3",
+        )
+    val = compute_m3_token_budget(store, "s-m3")
+    assert val == 2000.0
+
+
+def test_m3_token_budget_empty(tmp_path):
+    """No session_start_tokens -> 0."""
+    from iai_mcp.trajectory import compute_m3_token_budget
+
+    store = MemoryStore(path=tmp_path)
+    assert compute_m3_token_budget(store, "s-empty") == 0.0
+
+
+def test_session_exit_writes_trajectory_events(tmp_path, monkeypatch):
+    """session_exit dispatch writes trajectory_metric events (via core.py)."""
+    from iai_mcp.core import dispatch
+
+    store = MemoryStore(path=tmp_path)
+    # Call session_exit dispatch; it should call record_session_metrics
+    dispatch(store, "session_exit", {"session_id": "s-exit"})
+    # At minimum M1 should be recorded as 0 (no questions in this session)
+    events = query_events(store, kind="trajectory_metric")
+    # session_exit must emit trajectory events for the fresh session
+    session_events = [e for e in events if e.get("session_id") == "s-exit"]
+    assert len(session_events) >= 1