How to Use the Couchbase (Vector & NoSQL) MCP in LangChain

Q: Can I trace Couchbase (Vector & NoSQL) vector searches inside LangSmith?

Yes, every time your LangChain chain triggers vectorsearch, LangSmith logs the exact KNN inputs and outputs. You can monitor latency and token costs for every database operation in your pipeline.

Q: How does LangChain know which Couchbase (Vector & NoSQL) bucket to query?

The agent runs listbuckets first to discover the available routing spaces. It then uses that metadata to target the correct bucket for subsequent queries.

Q: Do I need to write custom LangChain tools for full-text search?

No, the ftssearch tool handles text extraction out of the box. Your agent calls it directly to match asynchronous content trees without custom python code.

Feed real-time Couchbase vector searches and NoSQL queries directly into your LangChain reasoning loops using this MCP Server.

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Connect Couchbase (Vector & NoSQL) MCP to LangChain

Create your Vinkius account to connect Couchbase (Vector & NoSQL) to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Couchbase (Vector & NoSQL) with LangChain

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Run Couchbase N1QL queries inside LangChain agents

The `execute_n1ql_query` tool lets your LangChain agent run structured queries against your Couchbase cluster to fetch JSON payloads on the fly. Instead of writing hardcoded database connectors, your agent uses this tool to inspect the schema and write the exact query it needs to satisfy the current step in your chain. You'll see these queries execute in real-time within your LangSmith traces. When the agent uses `list_buckets` to find the right data space and then queries it, you trace the exact latency and token usage of that specific database call.

Chain KNN vector searches with LangChain tools

The `vector_search` tool runs K-nearest neighbor searches directly on Couchbase vector indexes to feed raw context into your LLM prompts via the MCP standard. Your chain takes the output of this tool and immediately feeds it to the next step, avoiding any manual data shaping. This setup lets your LangChain agent run `list_indexes` to find the correct active search index before firing off the vector query. You get a clean pipeline where the model decides which index fits the user's intent.

Inspect Couchbase document structures on the fly

The `get_document` tool retrieves specific Couchbase KV documents directly into your LangChain agent's memory. This means your agent doesn't have to guess what fields are inside a document; it just grabs the raw JSON and inspects the properties. Combine this with `list_scopes` to let your agent map out the exact scope limits and collection boundaries in your database. It keeps your multi-step LangChain pipelines running with accurate database paths.

Setup guide

Set up Couchbase (Vector & NoSQL) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Couchbase (Vector & NoSQL) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "couchbase-vector-nosql-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Couchbase (Vector & NoSQL) transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Couchbase. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Couchbase (Vector & NoSQL) MCP in LangChain

LangChain catches the database exception directly through the MCP adapter. If `execute_n1ql_query` fails due to a bad filter, the agent reads the raw Couchbase error and rewrites the query automatically.

Yes, every time your LangChain chain triggers `vector_search`, LangSmith logs the exact KNN inputs and outputs. You can monitor latency and token costs for every database operation in your pipeline.

The agent runs `list_buckets` first to discover the available routing spaces. It then uses that metadata to target the correct bucket for subsequent queries.

No, the `fts_search` tool handles text extraction out of the box. Your agent calls it directly to match asynchronous content trees without custom python code.

Your Couchbase JSON documents and vector embeddings stay inside your local V8 isolate sandbox. Vinkius processes these values ephemerally, meaning no database records are ever stored or cached on our servers.

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