add basic MCP guide

docs/mkdocs.yml

@@ -162,6 +162,7 @@ nav:
               - Choosing right query type: guides/tuning_retrievers/1_query_types.md
               - Reranking: guides/tuning_retrievers/2_reranking.md
               - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
+          - Build MCP with LanceDB: guides/mcp.md
       - 🧬 Managing embeddings:
           - Understand Embeddings: embeddings/understanding_embeddings.md
           - Get Started: embeddings/index.md
@@ -293,6 +294,7 @@ nav:
           - Choosing right query type: guides/tuning_retrievers/1_query_types.md
           - Reranking: guides/tuning_retrievers/2_reranking.md
           - Embedding fine-tuning: guides/tuning_retrievers/3_embed_tuning.md
+      - - Build MCP with LanceDB: guides/mcp.md
   - Managing Embeddings:
       - Understand Embeddings: embeddings/understanding_embeddings.md
       - Get Started: embeddings/index.md

docs/src/guides/mcp.md

@@ -0,0 +1,113 @@
+# MCP server with LanceDB
+
+The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.
+
+With LanceDB, your MCP can be embedded in your application. Let's implement 2 simple MCP tools using LanceDB
+1. Add data - add data to LanceDB
+2. Retreive data - retrieve data from LanceDB
+
+You need to install `mcp[cli]` python package.
+
+First, let's define some configs:
+```python
+# mcp_server.py
+
+LANCEDB_URI = "~/lancedb"
+TABLE_NAME =  "mcp_data"
+EMBEDDING_FUNCTION = "sentence-transformers"
+MODEL_NAME = "all-MiniLM-L6-v2" 
+```
+
+Then initialize the table that we'll use to store and retreive data:
+```python
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+
+model = get_registry().get(EMBEDDING_FUNCTION).create(model_name=MODEL_NAME)
+
+class Schema(LanceModel):
+    text: str = model.SourceField()
+    vector: Vector(model.ndims()) = model.VectorField()
+
+db = lancedb.connect(LANCEDB_URI)
+if TABLE_NAME not in db.table_names():
+    db.create_table(TABLE_NAME, schema=Schema)
+```
+
+!!! Note "Using LanceDB cloud"
+    If you want to use LanceDB cloud, you'll need to set the uri to your remote table
+    instance and also provide a token. Every other functionality will remain the same
+
+## Defining the tools
+Tools let LLMs take actions through your server. There are other components like `resources` that allow you to expose certain data sources to LLMs. For our use case, we need to define tools that LLMs can call in order to inget or retrieve data
+We'll use `FastMCP` interface of the MCP package. The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing.
+
+```python
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("lancedb-example")
+```
+### Add data ingestion tool
+This function takes a string as input and adds it to the LanceDB table.
+
+```python
+@mcp.tool()
+async def ingest_data(content: str) -> str:
+    """
+    Add a new memory to the vector database
+    Args:
+        content: Content of the memory
+    """
+    tbl = db[TABLE_NAME]
+    tbl.add([
+        {"text": content}
+        ])
+    return f"Added memory: {content}"
+```
+### Retreive data tool
+
+This function takes a string and limit as input and searches the LanceDB table for the most relevant memories.
+
+```python
+@mcp.tool()
+async def retrieve_data(query: str, limit: int = 5) -> str:
+    """
+    Search db using vector search
+    Args:
+        query: The search query
+        limit: Maximum number of results to return
+    """
+    tbl = data[TABLE_NAME]
+    rs = tbl.search(query).limit(limit).to_list()
+    data = [
+        r["text"] for r in rs
+    ]
+    if not data:
+        return "No relevant data found."
+
+    return "\n\n".join(data)
+```
+
+## Install it on Claude desktop
+
+To install this MCP, you can simply run this command and it'll be registered on you Claude desktop
+```
+mcp install mcp_server.py
+```
+You'll see logs similar to this:
+```
+[04/07/25 20:18:08] INFO     Load pretrained SentenceTransformer: BAAI/bge-small-en-v1.5       SentenceTransformer.py:218
+Batches: 100%|█████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  4.06it/s]
+[04/07/25 20:18:11] INFO     Added server 'lancedb' to Claude config                                        claude.py:129
+                    INFO     Successfully installed lancedb in Claude app                                      cli.py:467
+```
+
+Now simply fire up claude desktop and you can start using it.
+
+## Community examples
+
+- Find a minimal LanceDB mcp server similar to this [here](https://github.com/kyryl-opens-ml/mcp-server-lancedb/blob/main/src/mcp_lance_db/server.py)
+
+- You can find an implementation of a more complex MCP server that uses LanceDB to implement an advanced CodeQA feature [here](https://github.com/lancedb/MCPExample).
+