Couchbase MCP Server

The Couchbase MCP Server bridges the gap between large language models and a Couchbase cluster, giving assistants instant, secure access to real‑time data without exposing raw credentials or query logic. By implementing the Model Context Protocol, it translates high‑level AI requests into native Couchbase operations—retrieving buckets, scopes, collections, and documents, as well as executing SQL++ queries—all while preserving the declarative, context‑driven flow that AI assistants expect.

For developers building data‑centric conversational experiences, this server removes the need to write custom database adapters or manage authentication tokens manually. Instead, an MCP client such as Claude Desktop can declare a “get‑bucket-list” or “run-query” intent, and the server handles authentication, permission checks, and query execution behind the scenes. This not only speeds up prototyping but also enforces consistent security policies, because every request is funneled through a single, auditable entry point.

Key capabilities include:

• Metadata discovery: list buckets, scopes, and collections to enable dynamic navigation of the database schema.
• CRUD operations: retrieve, upsert, or delete documents by ID within a specified scope and collection.
• Query execution: run SQL++ queries with an optional read‑only mode that blocks destructive operations, allowing safe data exploration.
• Health checks: expose server status and validate cluster credentials to surface connectivity issues early.

Typical use cases span from customer support bots that pull user profiles or order histories, to analytics assistants that generate real‑time reports from production data. In a multi‑tenant SaaS platform, the MCP server can enforce per‑client access controls by mapping scopes to tenant namespaces, ensuring that each assistant session operates only on its allotted data slice.

Integration is straightforward: once the MCP server is running, any MCP‑compatible client can reference it in its configuration. The client then calls tools such as or , and the server performs the operation, returning results in a structured format that the assistant can embed directly into responses. This seamless workflow eliminates round‑trips to external services, reduces latency, and keeps sensitive data within the controlled environment of the Couchbase cluster.