How to Use the Openli MCP in AutoGen

Q: How does AutoGen handle errors when calling Openli tools?

If a tool call like createagreement fails, the executing agent reports the error back to the group. The other agents can analyze the error message, adjust their inputs, and attempt the call again. This self-correcting behavior makes your compliance pipelines highly resilient.

Let your AutoGen agents debate and resolve complex legal compliance tasks using real-time Openli tools.

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Connect Openli MCP to AutoGen

Create your Vinkius account to connect Openli to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Openli with AutoGen

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Multi-agent consensus on privacy requests

Set up a collaborative team of agents to handle data subject requests. This MCP Server lets you set up a pipeline where one agent retrieves pending requests using `list_dsars`, while a legal-specialist agent reviews the details via `get_dsar`. They debate whether the request meets regulatory criteria before a third agent executes `create_dsar` to log the finalized task. This consensus-driven approach prevents automated mistakes on sensitive compliance issues. You don't have to rely on a single agent making an unchecked decision about your users' personal data.

Automated audit log verification

Keep your compliance records flawless with automated peer review. In AutoGen, you can have a security agent pull records using `list_audit_logs` while an auditor agent checks them against your internal database. They negotiate and flag any discrepancies they find in your consent history. If they find a missing record, they can coordinate to run `save_consent` to correct the log. This keeps your audit trail clean and ready for regulatory inspection without manual oversight.

Collaborative agreement drafting and review

Drafting terms of service doesn't have to be a manual chore. Your AutoGen agents can work together to draft new policies. A drafting agent creates the proposal, a compliance agent checks it against existing agreements using `list_agreements`, and a third agent registers the final version using `create_agreement`. This multi-step review process ensures your public terms are legally sound before they go live. The agents use `get_agreement` to verify the final layout and confirm the update was successful.

Setup guide

Set up Openli 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 Openli 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="Openli_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Openli. 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 Openli MCP in AutoGen

You register the server tools with your AssistantAgent using the AutoGen MCP adapter. This makes tools like `check_openli_status` available to all agents in your group chat. They can then call these tools cooperatively during their discussions.

Yes, you can design workflows where agents debate the validity of a request. For example, one agent can inspect a vendor using `get_vendor`, while another checks if they are authorized using `list_vendors`. They will only proceed once both agree on the vendor's compliance status.

If a tool call like `create_agreement` fails, the executing agent reports the error back to the group. The other agents can analyze the error message, adjust their inputs, and attempt the call again. This self-correcting behavior makes your compliance pipelines highly resilient.

Yes, you can assign specific tools to specific agents in your AutoGen configuration. For instance, you can allow only your compliance officer agent to execute `save_consent` or `create_dsar`, while other agents are limited to read-only queries.

Your audit logs and consent records are handled entirely within your local execution environment. When agents call `list_audit_logs` or `get_consent`, the sensitive data remains in your secure memory space. The MCP connection ensures that no compliance payloads are exposed to unauthorized external networks.

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