> ## Documentation Index
> Fetch the complete documentation index at: https://docs.raglight.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Agentic RAG & MCP

> Give your RAG agent access to external tools via MCP.

RAGLight supports the **Model Context Protocol (MCP)**, allowing your RAG pipeline to act as an agent that can query external databases, fetch live data, or execute code.

## MCP Configuration

In this example, we connect the RAG agent to a local MCP server running on port 8001.

```python agentic_rag.py theme={null}
from raglight.rag.simple_agentic_rag_api import AgenticRAGPipeline
from raglight.config.agentic_rag_config import AgenticRAGConfig
from raglight.config.vector_store_config import VectorStoreConfig
from raglight.config.settings import Settings
from raglight.models.data_source_model import FolderSource

Settings.setup_logging()

# 1. Vector store configuration
vector_store_config = VectorStoreConfig(
    embedding_model=Settings.DEFAULT_EMBEDDINGS_MODEL,
    provider=Settings.HUGGINGFACE,
    database=Settings.CHROMA,
    persist_directory="./defaultDb",
    collection_name=Settings.DEFAULT_COLLECTION_NAME,
)

# 2. MCP server connection (SSE transport)
mcp_servers = [
    {"url": "http://127.0.0.1:8001/sse"}
]

# 3. Configure Agentic RAG with MCP tools
config = AgenticRAGConfig(
    provider=Settings.OPENAI,
    model="gpt-4o",
    k=10,
    max_steps=4,
    mcp_config=mcp_servers,
    knowledge_base=[FolderSource(path="./company_data")],
)

# 4. Build and run
agent = AgenticRAGPipeline(config, vector_store_config)
agent.build()

# The agent now has access to both your documents AND the MCP tools
response = agent.generate(
    "Check the database for user 'Alice' and summarize her recent support tickets from the docs."
)
print(response)
```