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

Deploy teams of AutoGen agents that debate and agree on the best way to manage your Marqo vector indexes.

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

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

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Setup Marqo AI (Vector Search & Embeddings) with AutoGen

Ask AI about this MCP

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Claude

Perplexity

Let agents manage the index lifecycle

Delegate index management to a team of agents. A "provisioner" agent can propose running `create_index`. A "validator" agent can then double-check the work by calling `list_indexes` to confirm the new index exists before the next step begins. Once validated, a third "ingestion" agent can take over and use `add_documents` to populate the index. This conversational workflow, managed by this MCP Server, catches errors and ensures each step completes successfully before the next one starts.

Create consensus-driven search strategies

Build an agent team to run smarter searches. A "research" agent can run a broad `tensor_search`. A "cost-analysis" agent can simultaneously call `get_index_stats` to check the index size and query complexity, raising a flag if it's too expensive. These agents can debate the best course of action. Maybe they decide to refine the query, or maybe they conclude the search is worth the cost. You get a better, more considered result than a single agent would provide alone.

Automate risky operations with AutoGen

Use a multi-agent conversation to safeguard destructive actions. An agent can propose a plan that involves `delete_documents`. Before it can execute, a separate "auditor" agent must review the list of document IDs and give its approval. This creates a human-like review process, but fully automated. It's perfect for production environments where an accidental deletion could be a disaster. The agents debate the risk and proceed only when they reach a consensus.

Setup guide

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

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Marqo AI (Vector Search & Embeddings) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Marqo AI (Vector Search & Embeddings)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Marqo AI (Vector Search & Embeddings) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Marqo AI (Vector Search & Embeddings)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Marqo AI (Vector Search & Embeddings) data")
    print(result.messages[-1].content)

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.

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

You assign the Marqo tools to one or more agents in your group chat. One agent might be responsible for running `tensor_search`, while another is tasked with using `get_index_stats` to monitor performance, and they discuss the results.

Yes, this is a core strength of AutoGen. You can have one agent propose calling `delete_documents` with a list of IDs. Then, configure the group chat so that a second, "auditor" agent must explicitly approve the action before it can be executed.

You can build an autonomous maintenance crew. One agent detects an old index using `list_indexes`. It then discusses with another agent, which uses `get_index_stats` to confirm it has no documents, before they jointly decide to delete it.

Each agent has a prompt defining its role, and access to a set of tools. Based on the ongoing conversation and its specific job—like being a "security analyst" or "data engineer"—it decides which tool is most appropriate to contribute to the discussion.

Yes. The server only sees the data for each specific tool call, like the JSON payload for `add_documents`. Vinkius handles authentication and runs each request in an isolated sandbox, so your data isn't shared between tenants or stored after the call.

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