How to Use the Xata MCP in AutoGen

Q: How do I check all possible organizational deployments using Xata and AutoGen?

You use listorganizations to get a list of every org the user belongs to. Then, you run listprojects on each one to gather a complete view of deployed project containers for your multi-agent system.

Q: What tools should I use for auditing API key usage with Xata and AutoGen?

You start by listing all organization keys using listorgapikeys. You can then challenge the agents to compare that list against individual user access via listuserapikeys to spot potential overlaps or gaps.

Q: Can I identify available regions for deployment using Xata and AutoGen?

Absolutely. Running listregions provides a definitive list of all supported geographic areas. This data is critical input that the agents use to negotiate the optimal, compliant deployment location.

Q: What happens if I need to change organization details using Xata and AutoGen?

You call updateorganization, but an agent will likely challenge this action first. They'll confirm required changes—like updating the name or contact info—before committing, ensuring a deliberate process.

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Setup Xata with AutoGen

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Run security audits and key management debates with the MCP Server.

The multi-agent system uses this capability to debate access risk. One agent might run `list_organization_members` to check user roles, while a second agent runs `delete_user_api_keys` to simulate cleanup actions. They then negotiate whether deletion is necessary. This allows AutoGen to build systems where the answer isn't obvious and requires deliberation between competing perspectives on security policy.

Handle complex resource provisioning for Xata with MCP Server.

Deployment setup becomes a negotiation. One agent requests `create_project` because it needs new resources, while another checks existing capacity via `get_project_limits`. They must reach consensus on the correct project scope before proceeding. If you need to change deployment origins, they can debate mapping GitHub repos using `map_github_repository`, ensuring both agents agree on the source of truth.

Manage membership lifecycle through AutoGen's multi-agent framework.

The agent system debates best practices for user onboarding. One agent might suggest inviting a new member using `invite_organization_member`, while another reviews the current state via `list_organization_members`. This forces deliberation on who needs access and how. If an employee leaves, agents can agree to run `remove_organization_member` only after confirming their roles are deprecated.

Setup guide

Set up Xata 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 Xata 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="Xata_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Xata MCP in AutoGen

You use `list_organizations` to get a list of every org the user belongs to. Then, you run `list_projects` on each one to gather a complete view of deployed project containers for your multi-agent system.

You start by listing all organization keys using `list_org_api_keys`. You can then challenge the agents to compare that list against individual user access via `list_user_api_keys` to spot potential overlaps or gaps.

Absolutely. Running `list_regions` provides a definitive list of all supported geographic areas. This data is critical input that the agents use to negotiate the optimal, compliant deployment location.

You call `update_organization`, but an agent will likely challenge this action first. They'll confirm required changes—like updating the name or contact info—before committing, ensuring a deliberate process.

This server manages and exposes organizational membership lists (`list_organization_members`) and user API keys. The data type is structured access control information.

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