MCP For Claude Marketplace
Couchbase (Vector & NoSQL)

Couchbase (Vector & NoSQL) MCP Connector for Claude

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Manage vector search and NoSQL via Couchbase — execute N1QL queries, perform KNN vector searches, and audit documents directly from any AI agent.

7 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

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
nosqlvector-searchknnn1qlembeddingshigh-performance-computing

7 tools expose this connector's capabilities to your AI agent.

execute_n1ql_query

Provision a highly-available JSON Payload generating generic N1QL constraints

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

fts_search

Perform structural text-based extraction matching asynchronous Content Trees

get_document

Fetch elaborate internal mapped properties limiting Couchbase KV documents

vector_search

Execute static listing mapping structural KNN Vector similarities via Index

See how to talk to your AI agent using Couchbase (Vector & NoSQL).

List all search indexes in my cluster

I found 3 search indexes: 'vector-index-v1', 'fts-content-search', and 'customer-lookup'. Which one would you like to use for a search?

Find the top 3 similar products using this vector: [0.12, -0.5, 0.88]

Vector search complete! I found 3 matches in 'vector-index-v1'. The top result is 'Wireless Pro Headphones' with a similarity score of 0.98. Would you like to see the full document metadata?

Run N1QL query: 'SELECT name, price FROM `travel-sample` WHERE price < 100 LIMIT 5'

Query executed successfully! I've retrieved 5 items from 'travel-sample'. Results include 'Economy Flight' ($85) and 'Local Tour' ($45). I have the full JSON response if you need more details.

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.

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