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Vinkius

Couchbase (Vector & NoSQL) Connector for AI agents.

7 live capabilities

Query NoSQL data and perform KNN vector searches on your Couchbase cluster.

Live agent request Couchbase (Vector & NoSQL) / Connector

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AI Agent

Why people use Couchbase (Vector & NoSQL)

Couchbase (Vector & NoSQL) for faster RAG development

This Connector puts that entire process into a single conversation. You can ask your agent to run a vector_search and see the scores immediately, or use execute_n1ql_query to pull the exact JSON you need. You get the data you need right where you're working, without the manual copy-pasting.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a direct conversation line to your NoSQL and vector data without touching a terminal.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Debugging a RAG pipeline

    An AI engineer notices poor retrieval results and asks the agent to use vector_search to check the similarity scores of the top 5 results.

  2. Real-world use case 02

    Auditing a new production environment

    A data architect needs to verify the setup and asks the agent to use list_buckets and list_scopes to confirm the schema matches the staging plan.

  3. Real-world use case 03

    Quick JSON data extraction

    A product manager wants to see the structure of a specific user profile and asks the agent to use get_document to pull the JSON.

Complete set · 7capabilities

The complete Couchbase (Vector & NoSQL) capability set.

These are the exact actions your AI can choose when you ask it to work with Couchbase (Vector & NoSQL).

Capability set01 / 02

01—04

4 capabilities in this set.

Part of 7 available through Couchbase (Vector & NoSQL).

  1. 01 Capability

    Execute n1ql query

    Run N1QL queries to get specific JSON payloads from your buckets. It lets you perform complex data filtering using SQL-like logic.

  2. 02 Capability

    List buckets

    See all the routing spaces in your Couchbase DB. This helps you understand the high-level organization of your data.

  3. 03 Capability

    List scopes

    Find the limits and objects for your specific scopes and collections. It helps you see exactly what data is accessible.

  4. 04 Capability

    List indexes

    See all the active search indexes and rules attached to your data. This is vital for verifying your search configurations.

Capability set02 / 02

05—07

3 capabilities in this set.

Part of 7 available through Couchbase (Vector & NoSQL).

  1. 05 Capability

    Vector search

    Map structural KNN vector similarities using your existing vector indexes. This is the core capability for similarity-based retrieval.

  2. 06 Capability

    Get document

    Pull internal properties for specific Couchbase KV documents using their keys. It gives you an exact look at a single record.

  3. 07 Capability

    Fts search

    Match query strings against your content trees using full-text search indexes. It handles structural text-based extraction.

Set up in minutes

One URL. Then ask Couchbase (Vector & NoSQL) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Couchbase (Vector & NoSQL) from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_wWaJnEVjNwe96vxf5IajtX1h1rZH9VkCF8KWnsvT/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Couchbase (Vector & NoSQL), and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Couchbase (Vector & NoSQL) for the conversation.

Where the request belongs

Work Couchbase can move forward.

Built around the request

This is for the engineers and architects who are tired of manual schema audits and the headache of debugging RAG pipelines. It's for people who need to move fast between data exploration and production deployment.

01

AI Engineer

Debugging vector similarity scores and refining RAG prompts by inspecting real-time data.

02

Data Architect

Auditing NoSQL structures and verifying collection organization across different environments.

03

Database Administrator

Monitoring search indexes and running N1QL queries to ensure data consistency.

04

Product Manager

Prototyping search features and checking JSON schemas without waiting on a dev ticket.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
MongoDB Atlas Vector Search logo
01 6 capabilities

MongoDB Atlas Vector Search

Manage vector storage via MongoDB Atlas. perform similarity searches, query MQL documents, and audit collections.

View Connector
MyScale (SQL Vector Database API) logo
02 6 capabilities

MyScale (SQL Vector Database API)

Manage MyScale vector databases via SQL. execute queries, perform vector searches, and manage indices directly from any AI agent.

View Connector
Supabase Vector logo
03 7 capabilities

Supabase Vector

Connect your AI to Supabase Vector. Execute pgvector semantic searches, manage embeddings, and run relational database queries directly from your terminal.

View Connector
Qdrant logo
04 7 capabilities

Qdrant

Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.

View Connector
LanceDB (Serverless Vector DB) logo
05 6 capabilities

LanceDB (Serverless Vector DB)

Manage vectorized data via LanceDB. perform similarity searches, create tables, and manage multi-modal embeddings.

View Connector
Milvus (Open-Source Vector Database) logo
06 7 capabilities

Milvus (Open-Source Vector Database)

Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.

View Connector

Bring your own AI

Change the model, client or framework. Keep Couchbase connected.

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Before you connect

Questions about Couchbase.

The practical details behind the request, access and result.

Can I use the Couchbase (Vector & NoSQL) MCP to manage my RAG application?

Yes. It allows your AI agent to perform KNN vector searches and N1QL queries, making it much easier to debug and refine how your AI retrieves information from your database.

Does the Couchbase (Vector & NoSQL) MCP work with my self-hosted cluster?

It works with both Couchbase Capella and self-hosted clusters. You just need to provide your URL and credentials to give your agent access.

Can I use this to see my database structure?

Absolutely. You can ask the agent to list your buckets, scopes, and collections to get a clear picture of how your data is organized without running manual commands.

Is the Couchbase (Vector & NoSQL) MCP good for auditing indexes?

Yes, it's a great way to quickly enumerate all your search indexes and verify your vector definitions to ensure your search logic is set up correctly.

Can my AI agent actually run N1QL queries?

Yes, the Connector allows your agent to execute N1QL queries and return the JSON results directly in your chat, saving you from writing and testing queries manually.

How do I connect my Couchbase data to my AI client using this?

Once you subscribe on Vinkius, you just enter your database credentials. From there, your AI client can start querying your NoSQL and vector data immediately.

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' capability 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' capability 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.

One connection away

Give your agent a direct line to Couchbase.

Connect Couchbase once. Keep it beside 5,900+ managed Connectors when the next task needs more.

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