How to Use the Lucidworks Fusion (AI Search & Discovery) MCP in AutoGen

Q: How do AutoGen agents collaborate to query Lucidworks Fusion (AI Search & Discovery)?

One agent can initiate the query using lw.querysearch while a second agent evaluates the precision of the returned documents. If relevance is low, they can collaboratively refine the parameters and query again.

Q: Can I use this MCP Server to trigger document indexing from an AutoGen workflow?

Yes. An agent can call lw.indexdocuments to upload raw data and then run lw.listjobs to verify that the indexing pipeline completed successfully.

Q: How do agents resolve search profile conflicts in Lucidworks Fusion (AI Search & Discovery) using AutoGen?

Agents can use lw.listqueryprofiles to inspect available routes. The Lucidworks Fusion (AI Search & Discovery) MCP Server lets them programmatically compare profiles and choose the best path based on their built-in system instructions.

Run multi-agent debates in AutoGen to validate search relevance and index data using Lucidworks Fusion tools.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Lucidworks Fusion (AI Search & Discovery) MCP to AutoGen

Create your Vinkius account to connect Lucidworks Fusion (AI Search & Discovery) 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.

GDPR Free for Subscribers

Setup Lucidworks Fusion (AI Search & Discovery) with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Validate search quality with AutoGen multi-agent debate

This MCP Server exposes `lw.query_search` to let your agents run search queries against your active Fusion index profiles. In an AutoGen setup, a search specialist agent can execute the query while a critic agent reviews the output for relevance. If the results are too broad, the critic agent triggers `lw.query_filtered` to narrow down the search. They negotiate the best query parameters before presenting the final answer to the user.

Coordinate document ingestion across multiple AutoGen agents

The `lw.index_documents` tool sends raw files straight to your Fusion collection for indexing. An ingestion agent can push new documents, while a separate supervisor agent monitors the background tasks. By calling `lw.list_jobs`, the supervisor agent checks if ML training or ingestion pipelines are running hot. If a job fails, the agents can coordinate to retry the indexing step automatically.

Manage search relevance profiles using this MCP Server

The `lw.list_query_profiles` tool fetches all configured search routes on your Fusion instance. Your AutoGen agents can debate which profile fits a user's intent based on historical click patterns. When a decision is made, the execution agent uses `lw.post_signal` to record the outcome. This ensures your search engine learns from the multi-agent consensus.

Setup guide

Set up Lucidworks Fusion (AI Search & Discovery) 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 Lucidworks Fusion (AI Search & Discovery) 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="Lucidworks Fusion (AI Search & Discovery)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Lucidworks Fusion (AI Search & Discovery) data")
print(result.messages[-1].content)

Get your connection token →

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="Lucidworks Fusion (AI Search & Discovery)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Lucidworks Fusion (AI Search & Discovery) 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 Lucidworks Fusion. 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 Lucidworks Fusion (AI Search & Discovery) 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 Lucidworks Fusion (AI Search & Discovery) MCP in AutoGen

One agent can initiate the query using `lw.query_search` while a second agent evaluates the precision of the returned documents. If relevance is low, they can collaboratively refine the parameters and query again.

Yes. An agent can call `lw.index_documents` to upload raw data and then run `lw.list_jobs` to verify that the indexing pipeline completed successfully.

Agents can use `lw.list_query_profiles` to inspect available routes. The Lucidworks Fusion (AI Search & Discovery) MCP Server lets them programmatically compare profiles and choose the best path based on their built-in system instructions.

Your AutoGen supervisor agent will detect the failure status in the payload and can direct the ingestion agent to re-run the `lw.index_documents` tool.

Yes. Your search queries and job telemetry are protected by end-to-end token authentication managed directly by Vinkius. No raw credentials are saved, and the sandbox environment prevents cross-tenant data leaks.

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