diff --git a/src/tools.py b/src/tools.py
index 27bfa4e..d15e3f9 100644
--- a/src/tools.py
+++ b/src/tools.py
@@ -1,54 +1,50 @@
 """Tools for the RAG agent.
 
-This module defines two tools that the agent can call:
+This module defines two LangChain tools that interact with the
+``KnowledgeBase`` defined in :mod:`src.vector_store`.
 
-* ``search_knowledge_base`` – performs a semantic search in the Qdrant vector store.
-* ``add_to_knowledge_base`` – adds a document to the store.
-
-Both tools are decorated with ``@tool`` from ``langchain.tools`` so that the LLM can invoke them.
+The tools are decorated with ``@tool`` from ``langchain.tools`` so that
+the agent can invoke them automatically.
 """
 
-from __future__ import annotations
-
-from typing import List, Dict, Any
-
 from langchain.tools import tool
-
-# Import the knowledge base implementation.
-from .vector_store import KnowledgeBase
-
-# Create a global knowledge base instance. In a real application you might
-# want to inject this via a dependency injection container.
-kb = KnowledgeBase()
+from .vector_store import kb
 
 @tool("search_knowledge_base")
-def search_knowledge_base(query: str, max_results: int = 5) -> List[Dict[str, Any]]:
-    """Search the knowledge base for relevant chunks.
+def search_knowledge_base(query: str, max_results: int = 5) -> str:
+    """Search the local knowledge base.
 
     Parameters
     ----------
     query: str
-        Search query.
-    max_results: int
-        Number of results to return.
+        The search query.
+    max_results: int, optional
+        Limit of results to return.
 
     Returns
     -------
-    list[dict]
-        List of dictionaries with ``content``, ``title`` and ``chunk_index``.
+    str
+        A formatted string with the search results.
     """
-    return kb.search(query, limit=max_results)
+    results = kb.search(query, limit=max_results)
+    if not results:
+        return "No relevant documents found."
+    lines = []
+    for i, res in enumerate(results, 1):
+        title = res["metadata"].get("title", "Untitled")
+        lines.append(f"{i}. Title: {title}\nContent: {res['page_content']}\n")
+    return "\n".join(lines)
 
 @tool("add_to_knowledge_base")
 def add_to_knowledge_base(content: str, title: str) -> str:
-    """Add a document to the knowledge base.
+    """Add a new document to the knowledge base.
 
     Parameters
     ----------
     content: str
-        Full text of the document.
+        The full text of the document.
     title: str
-        Title or identifier for the document.
+        A short title for the document.
 
     Returns
     -------