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.

Couchbase (Vector & NoSQL) MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: Couchbase (Vector & NoSQL)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to Couchbase (Vector & NoSQL) through Vinkius, pass the Edge URL in the `mcps` parameter and every Couchbase (Vector & NoSQL) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Couchbase (Vector & NoSQL) Specialist",
    goal="Help users interact with Couchbase (Vector & NoSQL) effectively",
    backstory=(
        "You are an expert at leveraging Couchbase (Vector & NoSQL) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Couchbase (Vector & NoSQL) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Couchbase (Vector & NoSQL) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Couchbase (Vector & NoSQL) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Couchbase (Vector & NoSQL) MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 7 tools from Couchbase (Vector & NoSQL)

Why Use CrewAI with the Couchbase (Vector & NoSQL) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Couchbase (Vector & NoSQL) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Couchbase (Vector & NoSQL) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Couchbase (Vector & NoSQL) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Couchbase (Vector & NoSQL) for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Couchbase (Vector & NoSQL), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Couchbase (Vector & NoSQL) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Couchbase (Vector & NoSQL) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Couchbase (Vector & NoSQL) MCP Tools for CrewAI (7)

These 7 tools become available when you connect Couchbase (Vector & NoSQL) to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Couchbase (Vector & NoSQL) to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Couchbase (Vector & NoSQL) + CrewAI FAQ

Common questions about integrating Couchbase (Vector & NoSQL) MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.