Couchbase (Vector & NoSQL) MCP Server
Manage vector search and NoSQL via Couchbase — execute N1QL queries, perform KNN vector searches, and audit documents directly from any AI agent.
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What is the Couchbase MCP Server?
The Couchbase MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Couchbase via 7 tools. Manage vector search and NoSQL via Couchbase — execute N1QL queries, perform KNN vector searches, and audit documents directly from any AI agent. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (7)
Tools for your AI Agents to operate Couchbase
Ask your AI agent "List all search indexes in my cluster" and get the answer without opening a single dashboard. With 7 tools connected to real Couchbase data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Couchbase (Vector & NoSQL) MCP Server capabilities
7 toolsProvision a highly-available JSON Payload generating generic N1QL constraints
Perform structural text-based extraction matching asynchronous Content Trees
Fetch elaborate internal mapped properties limiting Couchbase KV documents
Identify bounded routing spaces inside the Headless Couchbase DB
Enumerate explicitly attached structured rules exporting active Search Indexes
Retrieve explicit UX logging tracing explicit Scope and Collection Object limits
Execute static listing mapping structural KNN Vector similarities via Index
What the Couchbase (Vector & NoSQL) MCP Server unlocks
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.
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
How it works
1. Subscribe to this server
2. Enter your Couchbase URL, Database Username, and Database Password
3. Start querying your NoSQL and vector data from Claude, Cursor, or any MCP-compatible client
Who is this for?
- AI Developers — test and debug vector similarity searches and semantic matching for RAG applications
- Data Architects — audit NoSQL data structures and verify collection organization across environments
- Database Administrators — monitor search indexes and execute N1QL queries to verify data consistency
- Product Teams — quickly prototype search features and audit JSON document schemas through natural language
Frequently asked questions about the Couchbase (Vector & NoSQL) MCP Server
Can my agent perform K-Nearest Neighbor (KNN) vector searches in Couchbase?
Yes. Provide the search index name, the vector embedding array, and the number of results (k). The agent uses Couchbase's native vector capabilities to locate the most semantically similar documents in your cluster.
How do I execute a N1QL query through the agent?
Use the 'execute_n1ql_query' tool and provide your SQL-like statement. The agent will fetch the structural JSON blocks directly from Couchbase, allowing you to perform complex data retrieval using familiar SQL syntax.
Can I search documents using full-text query logic?
Absolutely. The 'fts_search' tool leverages Couchbase's Full-Text Search (FTS) engine. Provide an index name and a boolean query string to perform structural text-based extraction across your document trees.
More in this category
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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.
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Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Give your AI agents the power of Couchbase MCP Server
Production-grade Couchbase (Vector & NoSQL) MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.