Couchbase (Vector & NoSQL) MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Couchbase (Vector & NoSQL) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Couchbase (Vector & NoSQL). "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Couchbase (Vector & NoSQL)?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Couchbase (Vector & NoSQL) MCP Server
Connect your Couchbase (Capella or self-hosted) cluster to any AI agent and take full control of your NoSQL and vector data through natural conversation.
LlamaIndex agents combine Couchbase (Vector & NoSQL) tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Vector Search (KNN) — Execute direct searches mapping AI embeddings to locate textual similarities using native vector indices
- N1QL SQL-for-JSON — Push absolute explicit querying using N1QL (SQL for Couchbase) to retrieve complex JSON structures across your buckets
- Document CRUD — Fetch elaborate internal properties and retrieve exact Data maps from specific collections using unique document keys
- Full-Text Search (FTS) — Perform structural text-based extraction matching query strings across advanced FTS search indexes
- Schema Navigation — Identify bounded routing spaces including Buckets, Scopes, and Collections to understand your data organization
- Index Auditing — Enumerate explicitly registered Search Indexes and verify vector definitions and cluster configurations
The Couchbase (Vector & NoSQL) MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Couchbase (Vector & NoSQL) to LlamaIndex via MCP
Follow these steps to integrate the Couchbase (Vector & NoSQL) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from Couchbase (Vector & NoSQL)
Why Use LlamaIndex with the Couchbase (Vector & NoSQL) MCP Server
LlamaIndex provides unique advantages when paired with Couchbase (Vector & NoSQL) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Couchbase (Vector & NoSQL) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Couchbase (Vector & NoSQL) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Couchbase (Vector & NoSQL), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Couchbase (Vector & NoSQL) tools were called, what data was returned, and how it influenced the final answer
Couchbase (Vector & NoSQL) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Couchbase (Vector & NoSQL) MCP Server delivers measurable value.
Hybrid search: combine Couchbase (Vector & NoSQL) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Couchbase (Vector & NoSQL) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Couchbase (Vector & NoSQL) for fresh data
Analytical workflows: chain Couchbase (Vector & NoSQL) queries with LlamaIndex's data connectors to build multi-source analytical reports
Couchbase (Vector & NoSQL) MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Couchbase (Vector & NoSQL) to LlamaIndex via MCP:
execute_n1ql_query
Provision a highly-available JSON Payload generating generic N1QL constraints
fts_search
Perform structural text-based extraction matching asynchronous Content Trees
get_document
Fetch elaborate internal mapped properties limiting Couchbase KV documents
list_buckets
Identify bounded routing spaces inside the Headless Couchbase DB
list_indexes
Enumerate explicitly attached structured rules exporting active Search Indexes
list_scopes
Retrieve explicit UX logging tracing explicit Scope and Collection Object limits
vector_search
Execute static listing mapping structural KNN Vector similarities via Index
Example Prompts for Couchbase (Vector & NoSQL) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Couchbase (Vector & NoSQL) immediately.
"List all search indexes in my cluster"
"Find the top 3 similar products using this vector: [0.12, -0.5, 0.88]"
"Run N1QL query: 'SELECT name, price FROM `travel-sample` WHERE price < 100 LIMIT 5'"
Troubleshooting Couchbase (Vector & NoSQL) MCP Server with LlamaIndex
Common issues when connecting Couchbase (Vector & NoSQL) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCouchbase (Vector & NoSQL) + LlamaIndex FAQ
Common questions about integrating Couchbase (Vector & NoSQL) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Couchbase (Vector & NoSQL) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Couchbase (Vector & NoSQL) to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.