How to Use the Jina AI (Search Foundation & LLM Grounding) MCP in AutoGen

Q: Can AutoGen agents use Jina AI (Search Foundation & LLM Grounding) to classify results?

Yes. A specialized agent can run classifytexts on retrieved data to sort it by topic before the rest of the group reviews it.

Coordinate agents using the Jina AI (Search Foundation & LLM Grounding) MCP Server in your AutoGen workflows.

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Connect Jina AI (Search Foundation & LLM Grounding) MCP to AutoGen

Create your Vinkius account to connect Jina AI (Search Foundation & LLM Grounding) 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 Jina AI (Search Foundation & LLM Grounding) with AutoGen

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Perplexity

Debate search findings in AutoGen

Assign `search_web_jina` to your research agent. If the findings conflict, your other agents can challenge the results and ask for a follow-up search. This creates a system that self-corrects. You aren't relying on a single pass; you are relying on a conversation.

Jina AI MCP Server for agent consensus

Use `rerank_documents` to provide a common set of facts for your debating agents. It ensures everyone is looking at the same high-quality context. When agents argue over the same data, they reach better conclusions. It turns a chaotic debate into a structured analysis.

Automated data processing

Your agents can trigger `generate_embeddings` when they agree on a final output. This allows your system to store its own discoveries automatically. It creates a feedback loop. The system learns from its own debates and stores the results as vectors.

Setup guide

Set up Jina AI (Search Foundation & LLM Grounding) 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 Jina AI (Search Foundation & LLM Grounding) 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="Jina AI (Search Foundation & LLM Grounding)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Jina AI (Search Foundation & LLM Grounding) 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="Jina AI (Search Foundation & LLM Grounding)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Jina AI (Search Foundation & LLM Grounding) data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Jina AI (Search Foundation & LLM Grounding) MCP in AutoGen

They call the tools as part of their conversation. If an agent needs a web search, it executes `search_web_jina` and presents the result to the group.

Yes. A specialized agent can run `classify_texts` on retrieved data to sort it by topic before the rest of the group reviews it.

You pass the tool list into your agent constructors. The adapter handles the rest, allowing agents to invoke search or reranking as needed.

It allows agents to break down massive reports into smaller pieces. This makes it easier for the team to analyze specific sections of a document.

Your endpoint token is stored in the Vinkius secure environment. No credentials are ever exposed to the agents or the LLM.

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