How to Use the Mockaroo MCP in AutoGen

Q: How do AutoGen agents coordinate Mockaroo data generation?

One agent calls listschemas to find templates, while another uses generatefromschema to execute the generation after validating the fields.

Q: Can a security agent block specific Mockaroo fields in AutoGen?

Yes, your security agent can inspect the output of listfieldtypes and reject any generation plan that uses restricted categories.

Q: Can I use custom datasets for multi-agent simulations?

Your agents call listdatasets to find existing CSV assets, then use them as context for generating realistic conversational inputs.

Q: How does AutoGen secure Mockaroo dataset payloads?

All CSV payloads retrieved via listdatasets are decrypted on the fly within our ephemeral sandbox. Your AutoGen agents inspect this data in memory, preventing any persistent logging of sensitive mock records.

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Setup Mockaroo with AutoGen

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Multi-agent schema validation with AutoGen

The `list_schemas` tool allows your AutoGen developer agent to retrieve templates, while a separate QA agent audits them for compliance. They negotiate the Mockaroo schema layout before executing any AutoGen data generation. This collaborative AutoGen workflow ensures that your generated Mockaroo data meets strict internal testing standards. The AutoGen agents resolve differences in Mockaroo schema expectations before calling the API.

Dynamic field negotiation via MCP Server

Calling `list_field_types` provides your AutoGen agents with a complete list of valid generation formats. An AutoGen security agent can flag forbidden fields while a data engineer agent selects realistic Mockaroo options from the list. Once consensus is reached, the lead AutoGen agent calls `generate_mock_data` to produce the final Mockaroo payload. This multi-agent AutoGen verification prevents invalid Mockaroo API requests and keeps data generation clean.

Collaborative dataset matching

The `list_datasets` tool lets your AutoGen team inspect existing Mockaroo reference files to ensure consistency across simulated environments. One AutoGen agent checks the file list while another runs `generate_from_schema` to match the target Mockaroo output. This AutoGen setup guarantees that relational integrity remains intact across multiple generated Mockaroo tables. The AutoGen agents debate the relationships and adjust the Mockaroo generation parameters automatically.

Setup guide

Set up Mockaroo 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 Mockaroo 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="Mockaroo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Mockaroo MCP in AutoGen

One agent calls `list_schemas` to find templates, while another uses `generate_from_schema` to execute the generation after validating the fields.

Yes, your security agent can inspect the output of `list_field_types` and reject any generation plan that uses restricted categories.

The Vinkius MCP Server queue manages concurrent calls from your AutoGen agents, ensuring stable execution of `generate_mock_data`.

Your agents call `list_datasets` to find existing CSV assets, then use them as context for generating realistic conversational inputs.

All CSV payloads retrieved via `list_datasets` are decrypted on the fly within our ephemeral sandbox. Your AutoGen agents inspect this data in memory, preventing any persistent logging of sensitive mock records.

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