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

# MCP Integration

> Exposing the RAG pipeline as Model Context Protocol (MCP) tools.

# MCP Integration

The Aris RAG pipeline is fully compliant with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io). This allows any MCP-enabled agent (Claude Desktop, internal agents) to utilize our vector stores and knowledge graphs as standard tools.

## Tool Definitions

We expose two primary tools via the MCP server: `retrieve_context` and `semantic_search`.

<CodeGroup>
  ```json retrieve_context (JSON-RPC) theme={null}
  {
    "name": "retrieve_context",
    "description": "Retrieves high-precision context for answering a user query using Hybrid Search and RRF.",
    "inputSchema": {
      "type": "object",
      "properties": {
        "query": {
          "type": "string",
          "description": "The user's natural language query."
        },
        "filters": {
          "type": "object",
          "description": "Metadata filters (e.g., source, author, date range).",
          "properties": {
            "source": { "type": "string" },
            "tags": { "type": "array", "items": { "type": "string" } }
          }
        },
        "top_k": {
          "type": "integer",
          "description": "Number of chunks to retrieve (default: 10).",
          "minimum": 1,
          "maximum": 50
        }
      },
      "required": ["query"]
    }
  }
  ```

  ```json semantic_search (JSON-RPC) theme={null}
  {
    "name": "semantic_search",
    "description": "Performs a raw vector similarity search without RRF or hybrid fusion. Use for exploration.",
    "inputSchema": {
      "type": "object",
      "properties": {
        "vector_query": {
          "type": "string",
          "description": "The text to embed and search."
        },
        "threshold": {
          "type": "number",
          "description": "Minimum similarity score (0.0 to 1.0).",
          "default": 0.7
        }
      },
      "required": ["vector_query"]
    }
  }
  ```
</CodeGroup>

## Server Implementation

Our MCP server is built using the `mcp-python-sdk`. It connects to the configured vector database (Qdrant/Pinecone) and manages the `FastAPI` lifecycle.

<Card title="MCP Server Lifecycle">
  1. **Initialize**: Connect to Vector DB and embedding model.
  2. **Expose Tools**: Register `retrieve_context` and `semantic_search` capabilities.
  3. **Handle Requests**: Validate JSON-RPC 2.0 requests.
  4. **Execute & Return**: Run the RAG pipeline and return `CallToolResult`.
</Card>

```python theme={null}
# Server-side implementation snippet
@mcp.tool()
async def retrieve_context(query: str, filters: dict = None, top_k: int = 10) -> str:
    """
    Executes the hybrid retrieval pipeline.
    """
    ctx.request_id = generate_uuid()
    
    # 1. Expand Query
    expanded_query = await query_rewriter.rewrite(query)
    
    # 2. Retrieve
    results = await retriever.hybrid_search(
        query=expanded_query,
        filters=filters,
        k=top_k
    )
    
    # 3. Format as Context String
    return format_results(results)
```

<Note>
  Ensure your MCP server has access to the same environment variables (OPENAI\_API\_KEY, VECTOR\_DB\_URL) as the main application.
</Note>