diff --git a/tools.py b/tools.py
new file mode 100644
index 0000000..47be5fe
--- /dev/null
+++ b/tools.py
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+"""
+Tools for the RAG agent.
+
+Two tools: search_knowledge_base and add_to_knowledge_base.
+"""
+
+from typing import List, Dict
+from langchain.tools import tool
+from vector_store import vector_store
+from chunker import split_text
+
+@tool("search_knowledge_base")
+def search_knowledge_base(query: str, max_results: int = 5) -> str:
+    """Semantic search in the knowledge base.
+
+    Returns a formatted string of results.
+    """
+    hits = vector_store.search(query, k=max_results)
+    if not hits:
+        return "No relevant documents found."
+    lines: List[str] = []
+    for i, hit in enumerate(hits, 1):
+        title = hit["metadata"].get("title", f"doc_{hit['id']}")
+        snippet = hit["document"][:200]
+        lines.append(f"{i}. {title}: {snippet}...")
+    return "\n".join(lines)
+
+@tool("add_to_knowledge_base")
+def add_to_knowledge_base(content: str, title: str = "document") -> str:
+    """Add a document to the knowledge base.
+
+    Splits content into chunks and stores each with metadata.
+    """
+    chunks = split_text(content)
+    for idx, chunk in enumerate(chunks):
+        doc_id = f"{title}_{idx}"
+        vector_store.add_document(doc_id=doc_id, text=chunk, metadata={"title": title})
+    return f"Added {len(chunks)} chunks from '{title}'."