Add testing

src/vector_search_mcp/__init__.py

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+from fastmcp.server.server import Transport
+
+from .main import mcp
+
+
+def run(transport: Transport = "sse"):
+    mcp.run(transport=transport)

src/vector_search_mcp/config.py

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+from vault_settings import VaultSettings
+
+
+class Settings(VaultSettings):
+    url: str
+    api_key: str | None = None

src/vector_search_mcp/engine.py

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+from collections.abc import Sequence
+from typing import Any, final
+
+from qdrant_client import AsyncQdrantClient, models
+
+from .config import Settings
+from .models import SearchRow
+
+
+@final
+class QdrantEngine:
+    def __init__(self) -> None:
+        self.settings = Settings()  # type: ignore[reportCallIssue]
+        self.client = AsyncQdrantClient(
+            url=self.settings.url, api_key=self.settings.api_key
+        )
+
+    async def semantic_search(
+        self,
+        embedding: Sequence[float] | models.NamedVector,
+        collection: str,
+        limit: int = 10,
+        conditions: Any | None = None,
+        threshold: float | None = None,
+    ) -> list[SearchRow]:
+        points = await self.client.search(
+            collection_name=collection,
+            query_vector=embedding,
+            query_filter=conditions,
+            limit=limit,
+            with_payload=True,
+            with_vectors=False,
+            score_threshold=threshold,
+        )
+
+        return [
+            SearchRow(chunk_id=str(point.id), score=point.score, payload=point.payload)
+            for point in points
+            if point.payload is not None
+        ]

src/vector_search_mcp/main.py

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+from fastmcp import FastMCP
+
+from .engine import QdrantEngine
+
+mcp = FastMCP("Vector Search MCP")
+
+engine = QdrantEngine()
+
+_ = mcp.tool(engine.semantic_search)

src/vector_search_mcp/models.py

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+from typing import Any
+
+from pydantic import BaseModel
+
+
+class SearchRow(BaseModel):
+    chunk_id: str
+    score: float
+    payload: dict[str, Any]