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_mcp_schema_list.py

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+"""Tests for schema_list dispatch.
+
+schema_list returns induced schemas with confidence + evidence + status.
+Supports domain + confidence_min filters.
+"""
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from uuid import uuid4
+
+import pytest
+
+from iai_mcp.core import dispatch
+from iai_mcp.store import MemoryStore
+from iai_mcp.types import EMBED_DIM, MemoryRecord
+
+
+@pytest.fixture(autouse=True)
+def _patch_embedder(monkeypatch):
+    from iai_mcp import embed as embed_mod
+
+    class _FakeEmbedder:
+        DIM = EMBED_DIM
+        DEFAULT_DIM = EMBED_DIM
+        DEFAULT_MODEL_KEY = "fake"
+
+        def __init__(self, *args, **kwargs):
+            self.DIM = EMBED_DIM
+
+        def embed(self, text: str) -> list[float]:
+            return [1.0] + [0.0] * (EMBED_DIM - 1)
+
+        def embed_batch(self, texts):
+            return [self.embed(t) for t in texts]
+
+    monkeypatch.setattr(embed_mod, "Embedder", _FakeEmbedder)
+    yield
+
+
+def _make_record(
+    *,
+    text: str = "r",
+    tags: list[str] | None = None,
+    detail_level: int = 2,
+    language: str = "en",
+) -> MemoryRecord:
+    now = datetime.now(timezone.utc)
+    return MemoryRecord(
+        id=uuid4(),
+        tier="episodic",
+        literal_surface=text,
+        aaak_index="",
+        embedding=[1.0] + [0.0] * (EMBED_DIM - 1),
+        community_id=None,
+        centrality=0.0,
+        detail_level=detail_level,
+        pinned=False,
+        stability=0.0,
+        difficulty=0.0,
+        last_reviewed=None,
+        never_decay=False,
+        never_merge=False,
+        provenance=[],
+        created_at=now,
+        updated_at=now,
+        tags=list(tags or []),
+        language=language,
+    )
+
+
+def test_schema_list_empty(tmp_path):
+    store = MemoryStore(path=tmp_path)
+    out = dispatch(store, "schema_list", {})
+    assert out == {"schemas": [], "total": 0}
+
+
+def test_schema_list_returns_persisted(tmp_path):
+    from iai_mcp.schema import SchemaCandidate, persist_schema
+
+    store = MemoryStore(path=tmp_path)
+    evidence = [_make_record(tags=["python", "web"]) for _ in range(3)]
+    for r in evidence:
+        store.insert(r)
+
+    cand = SchemaCandidate(
+        pattern="tags:python+web",
+        confidence=0.9,
+        evidence_count=3,
+        evidence_ids=[r.id for r in evidence],
+        status="auto",
+    )
+    persist_schema(store, cand)
+
+    out = dispatch(store, "schema_list", {})
+    assert out["total"] >= 1
+    s0 = out["schemas"][0]
+    assert "pattern" in s0
+    assert "confidence" in s0
+    assert "evidence_count" in s0
+    assert "status" in s0
+
+
+def test_schema_list_filter_confidence_min(tmp_path):
+    from iai_mcp.schema import SchemaCandidate, persist_schema
+
+    store = MemoryStore(path=tmp_path)
+    ev_a = [_make_record(tags=["python"]) for _ in range(2)]
+    for r in ev_a:
+        store.insert(r)
+    persist_schema(
+        store,
+        SchemaCandidate(
+            pattern="low-confidence",
+            confidence=0.7,
+            evidence_count=2,
+            evidence_ids=[r.id for r in ev_a],
+            status="pending_user_approval",
+        ),
+    )
+    ev_b = [_make_record(tags=["web"]) for _ in range(5)]
+    for r in ev_b:
+        store.insert(r)
+    persist_schema(
+        store,
+        SchemaCandidate(
+            pattern="high-confidence",
+            confidence=0.95,
+            evidence_count=5,
+            evidence_ids=[r.id for r in ev_b],
+            status="auto",
+        ),
+    )
+    out = dispatch(store, "schema_list", {"confidence_min": 0.85})
+    assert out["total"] == 1
+    assert out["schemas"][0]["pattern"] == "high-confidence"
+
+
+def test_schema_list_shape_has_exceptions_count(tmp_path):
+    """Schema entries always carry an exceptions_count key (0 when no exceptions)."""
+    from iai_mcp.schema import SchemaCandidate, persist_schema
+
+    store = MemoryStore(path=tmp_path)
+    ev = [_make_record(tags=["x"]) for _ in range(3)]
+    for r in ev:
+        store.insert(r)
+    persist_schema(
+        store,
+        SchemaCandidate(
+            pattern="tags:x",
+            confidence=0.9,
+            evidence_count=3,
+            evidence_ids=[r.id for r in ev],
+            status="auto",
+        ),
+    )
+    out = dispatch(store, "schema_list", {})
+    assert out["total"] >= 1
+    for s in out["schemas"]:
+        assert "exceptions_count" in s