How to Use the Marqo AI (Vector Search & Embeddings) MCP in CrewAI

Q: How do specialized CrewAI agents collaborate using Marqo AI (Vector Search & Embeddings)?

One agent can run tensorsearch to find relevant documents, while a second agent uses adddocuments to update the index based on new findings. They share the vector index state through their joint execution environment.

Q: Can I restrict which CrewAI agents have access to Marqo AI (Vector Search & Embeddings) tools over MCP?

Yes. You can use CrewAI's tool filtering to expose tensorsearch to your research agents while reserving deletedocuments and createindex for admin agents.

Q: What happens if an agent tries to write to a non-existent index?

The agent should call listindexes first to verify the target index exists. If it is missing, the agent can call createindex to build it before attempting to write documents.

Equip your CrewAI agent teams with semantic search capabilities to analyze and update Marqo vector indices.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Marqo AI (Vector Search & Embeddings) MCP to CrewAI

Create your Vinkius account to connect Marqo AI (Vector Search & Embeddings) to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Marqo AI (Vector Search & Embeddings) with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Collaborative search and analysis

The `tensor_search` tool equips your research agents with the ability to query vector databases using natural language. One agent can execute the search while a separate analyst agent parses the results. This division of labor produces deeper insights from your unstructured data. Because the search returns similarity scores, your agents can filter out irrelevant noise before passing findings to the next step. It makes your autonomous research loops far more precise.

Autonomous index maintenance

Running `delete_documents` allows a moderator agent to clean up outdated or low-quality vector data. A monitor agent first identifies stale records using search queries, then passes the IDs to the moderator. This keeps your index lean and fast without human oversight. This automated cleanup loop prevents vector drift over time. Your search results stay accurate because obsolete information is purged systematically.

Multi-agent catalog ingestion using this MCP Server

Adding new items requires invoking `add_documents` with clean metadata. Using this MCP Server, one agent can scrape raw text, another formats it into JSON, and a third writes it to Marqo. This pipeline handles large-scale ingestion smoothly. Before writing, the crew can check `get_index_stats` to verify storage limits. It ensures your autonomous operations never exceed their infrastructure budgets.

Setup guide

Set up Marqo AI (Vector Search & Embeddings) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Marqo AI (Vector Search & Embeddings) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Marqo AI (Vector Search & Embeddings) Analyst",
    goal="Access and analyze Marqo AI (Vector Search & Embeddings) data via MCP.",
    backstory="Expert analyst with direct Marqo AI (Vector Search & Embeddings) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Marqo AI (Vector Search & Embeddings) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Marqo AI (Vector Search & Embeddings) Analyst",
        goal="Access and analyze Marqo AI (Vector Search & Embeddings) data via MCP.",
        backstory="Expert analyst with direct Marqo AI (Vector Search & Embeddings) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Marqo AI (Vector Search & Embeddings) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Marqo AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Marqo AI (Vector Search & Embeddings) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Marqo AI (Vector Search & Embeddings) MCP in CrewAI

One agent can run `tensor_search` to find relevant documents, while a second agent uses `add_documents` to update the index based on new findings. They share the vector index state through their joint execution environment.

Yes. You can use CrewAI's tool filtering to expose `tensor_search` to your research agents while reserving `delete_documents` and `create_index` for admin agents.

Your agents manage rate limits through CrewAI's execution controls. The MCP Server processes requests sequentially, preventing your agents from overwhelming your Marqo endpoint during heavy parallel tasks.

The agent should call `list_indexes` first to verify the target index exists. If it is missing, the agent can call `create_index` to build it before attempting to write documents.

Yes. The MCP Server executes in an isolated sandbox that does not persist your payloads. Your search queries and document contents are sent directly to your Marqo instance via encrypted connections.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript