How to Use the Vald MCP in AutoGen

Q: If I need to add new facts during an AutoGen conversation, what tool do I use?

You call insertvector. This adds a unique vector and ID pair, immediately making that new fact available for all debating agents to reference.

Q: What is the risk of using Vald MCP Server with AutoGen?

The main risk involves deletion. deletevector permanently removes data. Always ensure that the knowledge being removed is outdated and unnecessary for future consensus.

Q: Can I use Vald's vector data in my AutoGen setup?

Yes, you can retrieve raw information using getvectordetails. This allows agents to inspect the exact content of a stored knowledge piece before deciding if it's relevant.

Q: If I need to check Vald's status with AutoGen?

Use getengineinfo. Calling this reports on the operational health of the entire MCP Server, letting you know if the underlying database is stable for complex multi-agent debates.

Drive consensus decisions using Vald and AutoGen.

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Setup Vald with AutoGen

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Ground Multi-Agent Debate with Vald

When agents debate, they need a source of truth. Use `search_vectors` to query the MCP Server for factual context based on a prompt vector. The consensus process can then validate or challenge these retrieved facts. This ensures that arguments aren't based on assumption but on data pulled directly from Vald.

Manage State Across AutoGen Agents

If an agent needs to confirm a starting state, it can call `get_vector_details` using a specific ID. This provides the raw vector data needed for deliberation. Additionally, if agents determine new facts during negotiation, they use `insert_vector` to add those vectors to the shared knowledge base.

System Validation and Clean-up via Vald

Before a critical decision, one agent should call `get_engine_info`. This tool reports on the operational health of the entire database engine. It's a necessary sanity check for AutoGen's complex deliberation cycles. If an outdated piece of information is found, another agent can use `delete_vector` to purge it permanently from the MCP Server.

Setup guide

Set up Vald 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 Vald 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="Vald_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Vald 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="Vald_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Vald data")
    print(result.messages[-1].content)

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Common questions about Vald MCP in AutoGen

Vald provides a factual layer. Agents query the MCP Server using `search_vectors`, and they must reconcile their conflicting arguments based on the data returned by Vald.

You call `insert_vector`. This adds a unique vector and ID pair, immediately making that new fact available for all debating agents to reference.

The main risk involves deletion. `delete_vector` permanently removes data. Always ensure that the knowledge being removed is outdated and unnecessary for future consensus.

Yes, you can retrieve raw information using `get_vector_details`. This allows agents to inspect the exact content of a stored knowledge piece before deciding if it's relevant.

Use `get_engine_info`. Calling this reports on the operational health of the entire MCP Server, letting you know if the underlying database is stable for complex multi-agent debates.

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