diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html
index f1bde880b..5df6db20c 100644
--- a/docs/_static/llama-stack-spec.html
+++ b/docs/_static/llama-stack-spec.html
@@ -11294,24 +11294,34 @@
                 "type": "object",
                 "properties": {
                     "query_generator_config": {
-                        "$ref": "#/components/schemas/RAGQueryGeneratorConfig"
+                        "$ref": "#/components/schemas/RAGQueryGeneratorConfig",
+                        "description": "Configuration for the query generator."
                     },
                     "max_tokens_in_context": {
                         "type": "integer",
-                        "default": 4096
+                        "default": 4096,
+                        "description": "Maximum number of tokens in the context."
                     },
                     "max_chunks": {
                         "type": "integer",
-                        "default": 5
+                        "default": 5,
+                        "description": "Maximum number of chunks to retrieve."
+                    },
+                    "chunk_template": {
+                        "type": "string",
+                        "default": "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n",
+                        "description": "Template for formatting each retrieved chunk in the context. Available placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk content string), {metadata} (chunk metadata dict). Default: \"Result {index}\\nContent: {chunk.content}\\nMetadata: {metadata}\\n\""
                     }
                 },
                 "additionalProperties": false,
                 "required": [
                     "query_generator_config",
                     "max_tokens_in_context",
-                    "max_chunks"
+                    "max_chunks",
+                    "chunk_template"
                 ],
-                "title": "RAGQueryConfig"
+                "title": "RAGQueryConfig",
+                "description": "Configuration for the RAG query generation."
             },
             "RAGQueryGeneratorConfig": {
                 "oneOf": [
diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml
index 10b5deec2..fb2dbf241 100644
--- a/docs/_static/llama-stack-spec.yaml
+++ b/docs/_static/llama-stack-spec.yaml
@@ -7794,18 +7794,37 @@ components:
       properties:
         query_generator_config:
           $ref: '#/components/schemas/RAGQueryGeneratorConfig'
+          description: Configuration for the query generator.
         max_tokens_in_context:
           type: integer
           default: 4096
+          description: Maximum number of tokens in the context.
         max_chunks:
           type: integer
           default: 5
+          description: Maximum number of chunks to retrieve.
+        chunk_template:
+          type: string
+          default: >
+            Result {index}
+
+            Content: {chunk.content}
+
+            Metadata: {metadata}
+          description: >-
+            Template for formatting each retrieved chunk in the context. Available
+            placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk
+            content string), {metadata} (chunk metadata dict). Default: "Result {index}\nContent:
+            {chunk.content}\nMetadata: {metadata}\n"
       additionalProperties: false
       required:
         - query_generator_config
         - max_tokens_in_context
         - max_chunks
+        - chunk_template
       title: RAGQueryConfig
+      description: >-
+        Configuration for the RAG query generation.
     RAGQueryGeneratorConfig:
       oneOf:
         - $ref: '#/components/schemas/DefaultRAGQueryGeneratorConfig'
diff --git a/docs/source/building_applications/rag.md b/docs/source/building_applications/rag.md
index db6303209..dbe90a7fc 100644
--- a/docs/source/building_applications/rag.md
+++ b/docs/source/building_applications/rag.md
@@ -51,6 +51,7 @@ chunks = [
         "mime_type": "text/plain",
         "metadata": {
             "document_id": "doc1",
+            "author": "Jane Doe",
         },
     },
 ]
@@ -98,6 +99,17 @@ results = client.tool_runtime.rag_tool.query(
 )
 ```
 
+You can configure how the RAG tool adds metadata to the context if you find it useful for your application. Simply add:
+```python
+# Query documents
+results = client.tool_runtime.rag_tool.query(
+    vector_db_ids=[vector_db_id],
+    content="What do you know about...",
+    query_config={
+        "chunk_template": "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n",
+    },
+)
+```
 ### Building RAG-Enhanced Agents
 
 One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example:
@@ -115,6 +127,12 @@ agent = Agent(
             "name": "builtin::rag/knowledge_search",
             "args": {
                 "vector_db_ids": [vector_db_id],
+                # Defaults
+                "query_config": {
+                    "chunk_size_in_tokens": 512,
+                    "chunk_overlap_in_tokens": 0,
+                    "chunk_template": "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n",
+                },
             },
         }
     ],
diff --git a/llama_stack/apis/tools/rag_tool.py b/llama_stack/apis/tools/rag_tool.py
index fdf199b1a..de3e4c62c 100644
--- a/llama_stack/apis/tools/rag_tool.py
+++ b/llama_stack/apis/tools/rag_tool.py
@@ -7,7 +7,7 @@
 from enum import Enum
 from typing import Annotated, Any, Literal
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, field_validator
 from typing_extensions import Protocol, runtime_checkable
 
 from llama_stack.apis.common.content_types import URL, InterleavedContent
@@ -67,11 +67,33 @@ register_schema(RAGQueryGeneratorConfig, name="RAGQueryGeneratorConfig")
 
 @json_schema_type
 class RAGQueryConfig(BaseModel):
+    """
+    Configuration for the RAG query generation.
+
+    :param query_generator_config: Configuration for the query generator.
+    :param max_tokens_in_context: Maximum number of tokens in the context.
+    :param max_chunks: Maximum number of chunks to retrieve.
+    :param chunk_template: Template for formatting each retrieved chunk in the context.
+        Available placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk content string), {metadata} (chunk metadata dict).
+        Default: "Result {index}\\nContent: {chunk.content}\\nMetadata: {metadata}\\n"
+    """
+
     # This config defines how a query is generated using the messages
     # for memory bank retrieval.
     query_generator_config: RAGQueryGeneratorConfig = Field(default=DefaultRAGQueryGeneratorConfig())
     max_tokens_in_context: int = 4096
     max_chunks: int = 5
+    chunk_template: str = "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n"
+
+    @field_validator("chunk_template")
+    def validate_chunk_template(cls, v: str) -> str:
+        if "{chunk.content}" not in v:
+            raise ValueError("chunk_template must contain {chunk.content}")
+        if "{index}" not in v:
+            raise ValueError("chunk_template must contain {index}")
+        if len(v) == 0:
+            raise ValueError("chunk_template must not be empty")
+        return v
 
 
 @runtime_checkable
diff --git a/llama_stack/providers/inline/tool_runtime/rag/memory.py b/llama_stack/providers/inline/tool_runtime/rag/memory.py
index 968f93354..39f752297 100644
--- a/llama_stack/providers/inline/tool_runtime/rag/memory.py
+++ b/llama_stack/providers/inline/tool_runtime/rag/memory.py
@@ -87,6 +87,7 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
                     content,
                     chunk_size_in_tokens,
                     chunk_size_in_tokens // 4,
+                    doc.metadata,
                 )
             )
 
@@ -142,19 +143,21 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
                 text=f"knowledge_search tool found {len(chunks)} chunks:\nBEGIN of knowledge_search tool results.\n"
             )
         ]
-        for i, c in enumerate(chunks):
-            metadata = c.metadata
+        for i, chunk in enumerate(chunks):
+            metadata = chunk.metadata
             tokens += metadata["token_count"]
+            tokens += metadata["metadata_token_count"]
+
             if tokens > query_config.max_tokens_in_context:
                 log.error(
                     f"Using {len(picked)} chunks; reached max tokens in context: {tokens}",
                 )
                 break
-            picked.append(
-                TextContentItem(
-                    text=f"Result {i + 1}:\nDocument_id:{metadata['document_id'][:5]}\nContent: {c.content}\n",
-                )
-            )
+
+            metadata_subset = {k: v for k, v in metadata.items() if k not in ["token_count", "metadata_token_count"]}
+            text_content = query_config.chunk_template.format(index=i + 1, chunk=chunk, metadata=metadata_subset)
+            picked.append(TextContentItem(text=text_content))
+
         picked.append(TextContentItem(text="END of knowledge_search tool results.\n"))
         picked.append(
             TextContentItem(
diff --git a/llama_stack/providers/utils/memory/vector_store.py b/llama_stack/providers/utils/memory/vector_store.py
index 9d892c166..e0e9d0679 100644
--- a/llama_stack/providers/utils/memory/vector_store.py
+++ b/llama_stack/providers/utils/memory/vector_store.py
@@ -139,22 +139,32 @@ async def content_from_doc(doc: RAGDocument) -> str:
         return interleaved_content_as_str(doc.content)
 
 
-def make_overlapped_chunks(document_id: str, text: str, window_len: int, overlap_len: int) -> list[Chunk]:
+def make_overlapped_chunks(
+    document_id: str, text: str, window_len: int, overlap_len: int, metadata: dict[str, Any]
+) -> list[Chunk]:
     tokenizer = Tokenizer.get_instance()
     tokens = tokenizer.encode(text, bos=False, eos=False)
+    try:
+        metadata_string = str(metadata)
+    except Exception as e:
+        raise ValueError("Failed to serialize metadata to string") from e
+
+    metadata_tokens = tokenizer.encode(metadata_string, bos=False, eos=False)
 
     chunks = []
     for i in range(0, len(tokens), window_len - overlap_len):
         toks = tokens[i : i + window_len]
         chunk = tokenizer.decode(toks)
+        chunk_metadata = metadata.copy()
+        chunk_metadata["document_id"] = document_id
+        chunk_metadata["token_count"] = len(toks)
+        chunk_metadata["metadata_token_count"] = len(metadata_tokens)
+
         # chunk is a string
         chunks.append(
             Chunk(
                 content=chunk,
-                metadata={
-                    "token_count": len(toks),
-                    "document_id": document_id,
-                },
+                metadata=chunk_metadata,
             )
         )
 
diff --git a/pyproject.toml b/pyproject.toml
index ee180c4c9..f1bf7384f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -320,3 +320,6 @@ ignore_missing_imports = true
 init_forbid_extra = true
 init_typed = true
 warn_required_dynamic_aliases = true
+
+[tool.ruff.lint.pep8-naming]
+classmethod-decorators = ["classmethod", "pydantic.field_validator"]
diff --git a/tests/integration/tool_runtime/test_rag_tool.py b/tests/integration/tool_runtime/test_rag_tool.py
index c49f507a8..2d049dc0c 100644
--- a/tests/integration/tool_runtime/test_rag_tool.py
+++ b/tests/integration/tool_runtime/test_rag_tool.py
@@ -49,7 +49,7 @@ def sample_documents():
     ]
 
 
-def assert_valid_response(response):
+def assert_valid_chunk_response(response):
     assert len(response.chunks) > 0
     assert len(response.scores) > 0
     assert len(response.chunks) == len(response.scores)
@@ -57,6 +57,11 @@ def assert_valid_response(response):
         assert isinstance(chunk.content, str)
 
 
+def assert_valid_text_response(response):
+    assert len(response.content) > 0
+    assert all(isinstance(chunk.text, str) for chunk in response.content)
+
+
 def test_vector_db_insert_inline_and_query(client_with_empty_registry, sample_documents, embedding_model_id):
     vector_db_id = "test_vector_db"
     client_with_empty_registry.vector_dbs.register(
@@ -77,7 +82,7 @@ def test_vector_db_insert_inline_and_query(client_with_empty_registry, sample_do
         vector_db_id=vector_db_id,
         query=query1,
     )
-    assert_valid_response(response1)
+    assert_valid_chunk_response(response1)
     assert any("Python" in chunk.content for chunk in response1.chunks)
 
     # Query with semantic similarity
@@ -86,7 +91,7 @@ def test_vector_db_insert_inline_and_query(client_with_empty_registry, sample_do
         vector_db_id=vector_db_id,
         query=query2,
     )
-    assert_valid_response(response2)
+    assert_valid_chunk_response(response2)
     assert any("neural networks" in chunk.content.lower() for chunk in response2.chunks)
 
     # Query with limit on number of results (max_chunks=2)
@@ -96,7 +101,7 @@ def test_vector_db_insert_inline_and_query(client_with_empty_registry, sample_do
         query=query3,
         params={"max_chunks": 2},
     )
-    assert_valid_response(response3)
+    assert_valid_chunk_response(response3)
     assert len(response3.chunks) <= 2
 
     # Query with threshold on similarity score
@@ -106,7 +111,7 @@ def test_vector_db_insert_inline_and_query(client_with_empty_registry, sample_do
         query=query4,
         params={"score_threshold": 0.01},
     )
-    assert_valid_response(response4)
+    assert_valid_chunk_response(response4)
     assert all(score >= 0.01 for score in response4.scores)
 
 
@@ -126,9 +131,6 @@ def test_vector_db_insert_from_url_and_query(client_with_empty_registry, sample_
     available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
     assert vector_db_id in available_vector_dbs
 
-    # URLs of documents to insert
-    # TODO: Move to test/memory/resources then update the url to
-    # https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/memory/resources/{url}
     urls = [
         "memory_optimizations.rst",
         "chat.rst",
@@ -155,7 +157,7 @@ def test_vector_db_insert_from_url_and_query(client_with_empty_registry, sample_
         vector_db_id=vector_db_id,
         query="What's the name of the fine-tunning method used?",
     )
-    assert_valid_response(response1)
+    assert_valid_chunk_response(response1)
     assert any("lora" in chunk.content.lower() for chunk in response1.chunks)
 
     # Query for the name of model
@@ -163,5 +165,69 @@ def test_vector_db_insert_from_url_and_query(client_with_empty_registry, sample_
         vector_db_id=vector_db_id,
         query="Which Llama model is mentioned?",
     )
-    assert_valid_response(response2)
+    assert_valid_chunk_response(response2)
     assert any("llama2" in chunk.content.lower() for chunk in response2.chunks)
+
+
+def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_id):
+    providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"]
+    assert len(providers) > 0
+
+    vector_db_id = "test_vector_db"
+
+    client_with_empty_registry.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model=embedding_model_id,
+        embedding_dimension=384,
+    )
+
+    available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
+    assert vector_db_id in available_vector_dbs
+
+    urls = [
+        "memory_optimizations.rst",
+        "chat.rst",
+        "llama3.rst",
+    ]
+    documents = [
+        Document(
+            document_id=f"num-{i}",
+            content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
+            mime_type="text/plain",
+            metadata={"author": "llama", "source": url},
+        )
+        for i, url in enumerate(urls)
+    ]
+
+    client_with_empty_registry.tool_runtime.rag_tool.insert(
+        documents=documents,
+        vector_db_id=vector_db_id,
+        chunk_size_in_tokens=512,
+    )
+
+    response_with_metadata = client_with_empty_registry.tool_runtime.rag_tool.query(
+        vector_db_ids=[vector_db_id],
+        content="What is the name of the method used for fine-tuning?",
+    )
+    assert_valid_text_response(response_with_metadata)
+    assert any("metadata:" in chunk.text.lower() for chunk in response_with_metadata.content)
+
+    response_without_metadata = client_with_empty_registry.tool_runtime.rag_tool.query(
+        vector_db_ids=[vector_db_id],
+        content="What is the name of the method used for fine-tuning?",
+        query_config={
+            "include_metadata_in_content": True,
+            "chunk_template": "Result {index}\nContent: {chunk.content}\n",
+        },
+    )
+    assert_valid_text_response(response_without_metadata)
+    assert not any("metadata:" in chunk.text.lower() for chunk in response_without_metadata.content)
+
+    with pytest.raises(ValueError):
+        client_with_empty_registry.tool_runtime.rag_tool.query(
+            vector_db_ids=[vector_db_id],
+            content="What is the name of the method used for fine-tuning?",
+            query_config={
+                "chunk_template": "This should raise a ValueError because it is missing the proper template variables",
+            },
+        )
diff --git a/tests/unit/rag/test_vector_store.py b/tests/unit/rag/test_vector_store.py
index 3decc431e..f97808a6d 100644
--- a/tests/unit/rag/test_vector_store.py
+++ b/tests/unit/rag/test_vector_store.py
@@ -12,7 +12,7 @@ from pathlib import Path
 import pytest
 
 from llama_stack.apis.tools import RAGDocument
-from llama_stack.providers.utils.memory.vector_store import URL, content_from_doc
+from llama_stack.providers.utils.memory.vector_store import URL, content_from_doc, make_overlapped_chunks
 
 DUMMY_PDF_PATH = Path(os.path.abspath(__file__)).parent / "fixtures" / "dummy.pdf"
 # Depending on the machine, this can get parsed a couple of ways
@@ -76,3 +76,53 @@ class TestVectorStore:
         )
         content = await content_from_doc(doc)
         assert content in DUMMY_PDF_TEXT_CHOICES
+
+    @pytest.mark.parametrize(
+        "window_len, overlap_len, expected_chunks",
+        [
+            (5, 2, 4),  # Create 4 chunks with window of 5 and overlap of 2
+            (4, 1, 4),  # Create 4 chunks with window of 4 and overlap of 1
+        ],
+    )
+    def test_make_overlapped_chunks(self, window_len, overlap_len, expected_chunks):
+        document_id = "test_doc_123"
+        text = "This is a sample document for testing the chunking behavior"
+        original_metadata = {"source": "test", "date": "2023-01-01", "author": "llama"}
+        len_metadata_tokens = 24  # specific to the metadata above
+
+        chunks = make_overlapped_chunks(document_id, text, window_len, overlap_len, original_metadata)
+
+        assert len(chunks) == expected_chunks
+
+        # Check that each chunk has the right metadata
+        for chunk in chunks:
+            # Original metadata should be preserved
+            assert chunk.metadata["source"] == "test"
+            assert chunk.metadata["date"] == "2023-01-01"
+            assert chunk.metadata["author"] == "llama"
+
+            # New metadata should be added
+            assert chunk.metadata["document_id"] == document_id
+            assert "token_count" in chunk.metadata
+            assert isinstance(chunk.metadata["token_count"], int)
+            assert chunk.metadata["token_count"] > 0
+            assert chunk.metadata["metadata_token_count"] == len_metadata_tokens
+
+    def test_raise_overlapped_chunks_metadata_serialization_error(self):
+        document_id = "test_doc_ex"
+        text = "Some text"
+        window_len = 5
+        overlap_len = 2
+
+        class BadMetadata:
+            def __repr__(self):
+                raise TypeError("Cannot convert to string")
+
+        problematic_metadata = {"bad_metadata_example": BadMetadata()}
+
+        with pytest.raises(ValueError) as excinfo:
+            make_overlapped_chunks(document_id, text, window_len, overlap_len, problematic_metadata)
+
+        assert str(excinfo.value) == "Failed to serialize metadata to string"
+        assert isinstance(excinfo.value.__cause__, TypeError)
+        assert str(excinfo.value.__cause__) == "Cannot convert to string"